References related to Many-objective Optimization


    Stats: Journal Count: 119, Conference Count: 138


    To Be Published

  1. Liu, R., Song, X., Fang, L., and Jiao, L., An r-Dominance-Based Preference Multi-Objective Optimization for Many-Objective Optimization, Soft Computing, To Be Published, : pp~

  2. Liu, Y., Gong, D., Sun, J., and Jin, Y., A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy, IEEE Transactions on Cybernetics, To Be Published, : pp~

  3. Bi, X., and Wang, C., An improved NSGA-III algorithm based on objective space decomposition for many-objective optimization, Soft Computing, To Be Published, : pp~

  4. Hu, W., Yen, G. G., and Luo, G., Many-Objective Particle Swarm Optimization Using Two-Stage Strategy and Parallel Cell Coordinate System, IEEE Transactions on Cybernetics, To Be Published, : pp~


  5. 2017

  6. Luo, N., Li, X., and Lin, Q., Objective Reduction For Many-Objective Optimization Problems Using Objective Subspace Extraction, Soft Computing, 2017, : pp~1-15

  7. Masood, A., Mei, Y., Chen, G., and Zhang, M., A PSO-Based Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling, Lecture Notes in Computer Science, 2017, 10142: pp~326-338

  8. Mohammadi, S., Monfared, M.A.S., and Bashiri, M., An Improved Evolutionary Algorithm For Handling Many-Objective Optimization Problems, Applied Soft Computing Journal, 2017, 52: pp~1239-1252

  9. Patel, V., Savsani, V., and Mudgal, A., Many-Objective Thermodynamic Optimization of Stirling Heat Engine, Energy, 2017, 125: pp~629-642

  10. Pholdee, N., Bureerat, S., and Yildiz, A.R., Hybrid Real-Code Population-Based Incremental Learning And Differential Evolution For Many-Objective Optimisation Of An Automotive Floor-Frame, International Journal of Vehicle Design, 2017, 73: pp~20-53

  11. Seada, H., Abouhawwash, M., and Deb, K., Towards A Better Balance of Diversity And Convergence In NSGA-III: First Results, Lecture Notes in Computer Science, 2017, 10173: pp~545-559

  12. Shen, R., Zheng, J., Li, M., and Zou, J., Many-Objective Optimization Based On Information Separation And Neighbor Punishment Selection, Soft Computing, 2017, 21: pp~1109-1128

  13. Tanabe, R., and Oyama, A., The Impact Of Population Size, Number Of Children, And Number Of Reference Points On The Performance Of NSGA-III, Lecture Notes in Computer Science, 2017, 10173: pp~606-621

  14. Tanabe, R., and Oyama, A., The impact of population size, number of children, and number of reference points on the performance of NSGA-III, Lecture Notes in Computer Science, 2017, 10173: pp~606-621

  15. Xiang, Y., Zhou, Y., Li, M., and Chen, Z., A Vector Angle-Based Evolutionary Algorithm For Unconstrained Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, 2017, 21: pp~131-152

  16. Abouhawwash, M., Seada, H., and Deb, K., Towards Faster Convergence Of Evolutionary Multi-Criterion Optimization Algorithms Using Karush Kuhn Tucker Optimality Based Local Search, Computers and Operations Research, 2017, 79: pp~331-346

  17. Bechikh, S., Elarbi, M., and Ben Said, L., Many-Objective Optimization Using Evolutionary Algorithms: A Survey, Adaptation, Learning, and Optimization, 2017, 20: pp~105-137

  18. Bhattacharjee, K.S., Singh, H.K., and Ray, T., A Novel Decomposition-Based Evolutionary Algorithm for Engineering Design Optimization, Journal of Mechanical Design, Transactions of the ASME, 2017, 139: pp~

  19. Duro, J.A., and Saxena, D.K., Timing The Decision Support For Real-World Many-Objective Optimization Problems, Lecture Notes in Computer Science, 2017, 10173: pp~191-205

  20. Dymond, A.S., Kok, S., and Heyns, P.S., MOTA: A Many-Objective Tuning Algorithm Specialized For Tuning Under Multiple Objective Function Evaluation Budgets, Evolutionary Computation, 2017, 25: pp~113-141

  21. Falcón-Cardona, J.G., and Coello Coello, C.A., A New Indicator-Based Many-Objective Ant Colony Optimizer For Continuous Search Spaces, Swarm Intelligence, 2017, 11: pp~71-100

  22. Freire, H., Moura Oliveira, P.B., and Solteiro Pires, E.J., From Single to Many-Objective PID Controller Design Using Particle Swarm Optimization, International Journal of Control, Automation and Systems, 2017, : pp~1-15

  23. Gonçalves, R.A., Pavelski, L.M., de Almeida, C.P., Kuk, J.N., Venske, S.M., and Delgado, M.R., Adaptive Operator Selection For Many-Objective Optimization With NSGA-III, Lecture Notes in Computer Science, 2017, 10173: pp~267-281

  24. Gong, D., Sun, F., Sun, J., and Sun, X., Set-Based Many-Objective Optimization Guided By A Preferred Region, Neurocomputing, 2017, 228: pp~241-255

  25. Ibrahim, A., Rahnamayan, S., Martin, M.V., and Deb, K., Fusion Of Many-Objective Non-Dominated Solutions Using Reference Points, Lecture Notes in Computer Science, 2017, 10173: pp~314-328


  26. 2016

  27. Ishibuchi, H., Setoguchi, Y., Masuda, H., and Nojima, Y., How To Compare Many-Objective Algorithms Under Different Settings Of Population And Archive Sizes, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1149-1156

  28. Jaimes, A.L., and García-Nájera, A., Discrete Many-Objective Optimization Problems: The Case Of The Pickup And Delivery Problem, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1123-1130

  29. Li, B., Tang, K., Li, J., and Yao, X., Stochastic Ranking Algorithm For Many-Objective Optimization Based On Multiple Indicators, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~924-938

  30. Li, X., Zeng, S., Zhang, L., and Zhang, G., Combining Dynamic Constrained Many-Objective Optimization With DE to Solve Constrained Optimization Problems, Communications in Computer and Information Science, 2016, 575: pp~64-73

  31. Li, Y., Liu, H.-L., and Gu, F., An Objective Reduction Algorithm Based On Hyperplane Approximation For Many-Objective Optimization Problems, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~2470-2476

  32. Liu, H.-L., Chen, L., Zhang, Q., and Deb, K., An Evolutionary Many-Objective Optimisation Algorithm With Adaptive Region Decomposition, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~4763-4769

  33. Ma, X., Yang, J., Wu, N., Ji, Z., and Zhu, Z., A Comparative Study On Decomposition-Based Multi-Objective Evolutionary Algorithms For Many-Objective Optimization, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~2477-2483

  34. Powell, C., Thant, P.T., and Munetomo, M., Evaluation Of Three Steady-State NSGA-III Offspring Selection Schemes For Many-Objective Optimization, Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016, 2016, : pp~166-171

  35. Rad, M.A., and Hamzeh, A., A Coevolutionary Approach to Many Objective Optimization Based on a Novel Ranking Method, Intelligent Data Analysis, 2016, 20: pp~129-151

  36. Reynoso-Meza, G., Sanchis, J., Blasco, X., and Freire, R.Z., Evolutionary Multi-Objective Optimisation with Preferences for Multivariable PI Controller Tuning, Expert Systems with Applications, 2016, 51: pp~120-133

  37. Sato, H., Chain-Reaction Solution Update In MOEA/D And Its Effects On Multi- And Many-Objective Optimization, Soft Computing, 2016, 20: pp~3803-3820

  38. Sato, H., Nakagawa, S., Miyakawa, M., and Takadama, K., Enhanced Decomposition-Based Many-Objective Optimization Using Supplemental Weight Vectors, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1626-1633

  39. Saxena, D. K., Sinha, A., Duro, J. A., and Zhang, Q., Entropy-Based Termination Criterion For Multiobjective Evolutionary Algorithms, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~485-498

  40. Seada, H., and Deb, K., A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~358-359

  41. Silva, R., Salimi, A., Li, M., Freitas, A., Guimaraes, F., and Lowther, D., Visualization and Analysis of Tradeoffs in Many-Objective Optimization: A Case Study on the Interior Permanent Magnet Motor Design, IEEE Transactions on Magnetics, 2016, 52: pp~1-4

  42. Smith, R., Kasprzyk, J., and Zagona, E., Many-Objective Analysis to Optimize Pumping and Releases in Multi-Reservoir Water Supply Network, Journal of Water Resources Planning and Management, 2016, 142: pp~

  43. Starkey, A., Hagras, H., Shakya, S., and Owusu, G., A Multi-Objective Genetic Type-2 Fuzzy Logic Based System for Mobile Field Workforce Area Optimization, Information Sciences, 2016, 329: pp~390-411

  44. Sun, X., Chen, Y., Liu, Y., and Gong, D., Indicator-Based Set Evolution Particle Swarm Optimization for Many-Objective Problems, Soft Computing, 2016, 20: pp~2219-2232

  45. Tatsukawa, T., Watanabe, T., and Oyama, A., Evolutionary Computation For Many-Objective Optimization Problems Using Massive Population Sizes On The K Supercomputer, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1139-1148

  46. Toscano, G., and Deb, K., Study Of The Approximation Of The Fitness Landscape And The Ranking Process Of Scalarizing Functions For Many-Objective Problems, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~4358-4365

  47. Wang, H., and Yao, X., Objective Reduction Based on Nonlinear Correlation Information Entropy, Soft Computing, 2016, 20: pp~2393-2407

  48. Wang, Q., Liu, H.-L., and Cheung, Y.-M., A Renewable Energy Cooperation Scheme For OFDM Systems Using Evolutionary Many-Objective Optimization Algorithm, Proceedings - 12th International Conference on Computational Intelligence and Security, CIS 2016, 2016, : pp~194-197

  49. Yang, S., Wang, J., and Liu, Q., Multiple-Objective Optimization Based On A Two-Time-Scale Neurodynamic System, Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016, 2016, : pp~193-199

  50. Yuan, J., and Liu, H.-L., A New Dominance Relation Based On Simplex For Many Objective Optimization Problems, Proceedings - 12th International Conference on Computational Intelligence and Security, CIS 2016, 2016, : pp~175-178

  51. Yuan, Y., Xu, H., Wang, B., and Yao, X., A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~16-37

  52. Zhang, B., Shafi, K., and Abbass, H.A., Hybrid Knowledge-Based Evolutionary Many-Objective Optimization, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1007-1014

  53. Zhang, Y., and Bi, X., Optimization Design of Shape-Shifting Robot Based on Many-Objective Directional Hybrid Evolutionary Algorithm, Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2016, 52: pp~72-78

  54. Zhang, Y.-H., Gong, Y.-J., Zhang, J., and Ling, Y.-B., A Hybrid Evolutionary Algorithm With Dual Populations For Many-Objective Optimization, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1610-1617

  55. Zhu, C., Cai, X., Fan, Z., and Sulaman, M., A Two-Phase Many-Objective Evolutionary Algorithm With Penalty Based Adjustment For Reference Lines, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~2161-2168

  56. Zhu, C., Xu, L., and Goodman, E. D., Generalization of Pareto-Optimality for Many-Objective Evolutionary Optimization, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~299-315

  57. Bazargan Lari, K., and Hamzeh, A., A diversity control mechanism in many objective optimizations, Applied Intelligence, 2016, 45: pp~953-975

  58. Botelho, G., Britto, A., and Silva, L., A New Estimation Distributed Algorithm Applied To A Many-Objective Discrete Optimization Problem, Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016, 2016, : pp~415-420

  59. Castro Jr., O. R., Santana, R., and Pozo,A., C-Multi: A Competent Multi-Swarm Approach for Many-Objective Problems , Neurocomputing , 2016, 180: pp~68 - 78

  60. Cheng, R., Jin, Y., Olhofer, M., and Sendhoff, B., A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~773-791

  61. Cheung, Y.-M., Gu, F., and Liu, H.-L., Objective Extraction For Many-Objective Optimization Problems: Algorithm And Test Problems, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~755-772

  62. Chong, J.K., and Tan, K.C., A Novel Grid-Based Differential Evolution (DE) Algorithm For Many-Objective Optimization, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~2776-2783

  63. Copado-Mendez, P.J., Pozo, C., Guillen-Gosalbez, G., and Jiménez, L., Enhancing the ε-Constraint Method Through the Use of Objective Reduction and Random Sequences: Application to Environmental Problems, Computers and Chemical Engineering, 2016, 87: pp~36-48

  64. Dai, C., Wang, Y., and Hu, L., An Improved Alpha-Dominance Strategy for Many-Objective Optimization Problems, Soft Computing, 2016, 20: pp~1105-1111

  65. Drechsler, N., Self-Adaptive Evolutionary Many-Objective Optimization Based on Relation Epsilon-Preferred, Studies in Computational Intelligence, 2016, 613: pp~23-37

  66. Goncalves, R.A., Almeida, C.P., Pavelski, L.M., Venske, S.M., Kuk, J.N., and Pozo, A.T., Adaptive Operator Selection In NSGA-III, Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016, 2016, : pp~181-186

  67. Goulart, F., and Campelo, F., Preference-Guided Evolutionary Algorithms for Many-Objective Optimization, Information Sciences, 2016, 329: pp~236-255

  68. Guo, X., Wang, Y., and Wang, X., An Objective Reduction Algorithm Using Representative Pareto Solution Search For Many-Objective Optimization Problems, Soft Computing, 2016, 20: pp~4881-4895

  69. Haghighi, A., and Ayati, A.H., Stability Analysis of Gravity Dams Under Uncertainty Using the Fuzzy Sets Theory and a Many-Objective GA, Journal of Intelligent and Fuzzy Systems, 2016, 30: pp~1857-1868

  70. He, Z., and Yen, G.G., Visualization and Performance Metric in Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~386-402

  71. He, Z., and Yen, G.G., An Improved Visualization Approach In Many-Objective Optimization, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1618-1625

  72. He, Z., and Yen, G. G. , Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement, IEEE Transactions on Evolutionary Computation, 2016, 20: pp~145-160

  73. Hierons, R.M., Li, M., Liu, X., Segura, S., and Zheng, W., SIP: Optimal Product Selection From Feature Models Using Many-Objective Evolutionary Optimization, ACM Transactions on Software Engineering and Methodology, 2016, 25: pp~

  74. Ibrahim, A., Rahnamayan, S., Martin, M.V., and Deb, K., 3D-RadVis: Visualization Of Pareto Front In Many-Objective Optimization, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~736-745

  75. Ibrahim, A., Rahnamayan, S., Martin, M.V., and Deb, K., EliteNSGA-III: An improved Evolutionary Many-Objective Optimization Algorithm, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~973-982

  76. Ishibuchi, H., Doi, K., and Nojima, Y., Characteristics Of Many-Objective Test Problems And Penalty Parameter Specification In MOEA/D, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 2016, : pp~1115-1122


  77. 2015

  78. Lei, Y., Jiang, W., Liu, L., and Ma, X., Many-Objective Optimization Based on Sub-Objective Evolutionary Algorithm, Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2015, 41: pp~1910-1917

  79. Li, B., Li, J., Tang, K., and Yao, X., Many-Objective Evolutionary Algorithms: A Survey, ACM Comput. Surv., 2015, 48: pp~13:1--13:35

  80. Li, K., Deb, K., Zhang, Q., and Kwong, S., An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~694-716

  81. Li, M., Yang, S., and Liu, X., Bi-Goal Evolution for Many-Objective Optimization Problems , Artificial Intelligence , 2015, 228: pp~45 - 65

  82. Li, Y., Liu, H., Xie, K., and Yu, X., A Method for Distributing Reference Points Uniformly Along the Pareto Front of DTLZ Test Functions in Many-Objective Evolutionary Optimization, Information Science and Technology (ICIST), 2015 5th International Conference on, 2015, : pp~541-546

  83. Luo, C., Shimoyama, K., and Obayashi, S., Effects of the Number of Design Variables on Performances in Kriging-Model-Based Many-Objective Optimization, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~1901-1908

  84. Matrosov, E.S., Huskova, I., Kasprzyk, J.R., Harou, J.J., Lambert, C., and Reed, P.M., Many-Objective Optimization and Visual Analytics Reveal Key Trade-Offs for London’s Water Supply , Journal of Hydrology , 2015, 531, Part 3: pp~1040 - 1053

  85. Maulana, A., Jiang, Z., Liu, J., Back, T., and Emmerich, M.T.M., Reducing Complexity in Many Objective Optimization Using Community Detection, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~3140-3147

  86. Mkaouer, M.W., Kessentini, M., Bechikh, S., Ó Cinnéide, M., and Deb, K., On the Use of Many Quality Attributes for Software Refactoring: A Many-Objective Search-Based Software Engineering Approach, Empirical Software Engineering, 2015, : pp~1-43

  87. Nunez, T., Ayala, V., Paciello, J., and Baran, B., Protection with Quality of Service in Optical WDM Networks Using Many-Objective Ant Colony Optimization, Computing Conference (CLEI), 2015 Latin American, 2015, : pp~1-12

  88. Panichella, A., Kifetew, F.M., and Tonella, P., Reformulating Branch Coverage as a Many-Objective Optimization Problem, Software Testing, Verification and Validation (ICST), 2015 IEEE 8th International Conference on, 2015, : pp~1-10

  89. Qin, S., Zeng, S., Dong, W., and Li, X., Nonlinear Equation Systems Solved by Many-Objective Hype, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~2691-2696

  90. Ramirez, A., Romero, J.R., and Ventura, S., A Comparative Study of Many-Objective Evolutionary Algorithms for the Discovery of Software Architectures, Empirical Software Engineering, 2015, : pp~

  91. Rivera-Zamarripa, L., Roberts, S., and Cruz-Cortés, N., Analysis and Visualization of a Many-Objective Optimization Landscape Design Problem, Interdisciplinary Topics in Applied Mathematics, Modeling and Computational Science, 2015, : pp~375-380

  92. Rodemann, T., Narukawa, K., Fischer, M., and Awada, M., Many-Objective Optimization of a Hybrid Car Controller, Applications of Evolutionary Computation, 2015, : pp~593-603

  93. Roy, P.C., Islam, M.M., Murase, K., and Yao, X., Evolutionary Path Control Strategy for Solving Many-Objective Optimization Problem, IEEE Transactions on Cybernetics, 2015, 45: pp~702-715

  94. Sato, H., MOEA/D using Constant-Distance Based Neighbors Designed for Many-Objective Optimization, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~2867-2874

  95. Sato, H., Analysis of Inverted PBI and Comparison with Other Scalarizing Functions in Decomposition Based MOEAs, Journal of Heuristics, 2015, 21: pp~819-849

  96. Schlueter, M., Yam, C., Watanabe, T., and Oyama, A., Many-Objective Optimization of Interplanetary Space Mission Trajectories, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~3256-3262

  97. Seada, H., and Deb, K., Effect of Selection Operator on NSGA-III in Single, Multi, and Many-Objective Optimization, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~2915-2922

  98. Shen, R., Zheng, J., Li, M., and Zou, J., Many-Objective Optimization Based on Information Separation and Neighbor Punishment Selection, Soft Computing, 2015, : pp~1-20

  99. Shimizu, Y., An Extension of the MOON2/MOON2R Approach to Many-Objective Optimization Problems, Optimization Methods, Theory and Applications, 2015, : pp~51-65

  100. Singh, H.K., Development of Optimization Methods to Deal with Current Challenges in Engineering Design Optimization, AI Communications, 2015, 29: pp~219-221

  101. Singh, H., Asafuddoula, M., Alam, K., and Ray, T., Re-design for Robustness: An Approach Based on Many Objective Optimization, Evolutionary Multi-Criterion Optimization, 2015, : pp~343-357

  102. Tsang, W.W.P., and Lau, H.Y.K., A Grid-Facilitated AIS-Based Network Scheme for Manyobjective Optimization, GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, 2015, : pp~1497-1498

  103. Von Lucken, C., Monzon, H., Brizuela, C., and Baran, B., Dimensionality Reduction in Many-Objective Problems Combining PCA and Spectral Clustering, GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, 2015, : pp~1511-1512

  104. Wagner, M., Bringmann, K., Friedrich, T., and Neumann, F., Efficient Optimization of Many Objectives by Approximation-Guided Evolution , European Journal of Operational Research , 2015, 243: pp~465 - 479

  105. Wang, H., He, S., and Yao, X., Nadir Point Estimation for Many-Objective Optimization Problems Based on Emphasized Critical Regions, Soft Computing, 2015, : pp~1-13

  106. Wang, H., Jiao, L., and Yao, X., Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~524-541

  107. Yazawa, Y., Aguirre, H., Oyama, A., and Tanaka, K., Evolutionary Many-Objective Optimization Using Dynamic Epislon-Hoods and Chebyshev Function, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~1861-1868

  108. Yu, Y., Ma, H., and Zhang, M., F-MOGP: A Novel Many-Objective Evolutionary Approach to QoS-aware Data Intensive Web Service Composition, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~2843-2850

  109. Yuan, Y., Xu, H., and Wang, B., An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization, Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015, : pp~775-782

  110. Yuan, Y., Xu, H., Wang, B., Zhang, B., and Yao, X., Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers, Evolutionary Computation, IEEE Transactions on, 2015, PP: pp~1-1

  111. Zhang, X., Tian, Y., and Jin, Y., A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~761-776

  112. Zheng, J., Bai, H., Shen, R., and Li, M., A Comparative Study Use of OTL for Many-Objective Optimization, GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, 2015, : pp~1411-1412

  113. Zhou, Y., Wang, J., Chen, J., Gao, S., and Teng, L., Ensemble of Many-Objective Evolutionary Algorithms for Many-Objective Problems, Soft Computing, 2015, : pp~1-13

  114. Asafuddoula, M., Ray, T., Isaacs, A., and Singh, H.K., Performance of a Steady State Quantum Genetic Algorithm for Multi/Many-Objective Engineering Optimization Problems, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~893-899

  115. Asafuddoula, M., Ray, T., and Sarker, R., A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~445-460

  116. Asafuddoula, M., Singh, H.K., and Ray, T., Six-Sigma Robust Design Optimization Using a Many-Objective Decomposition-Based Evolutionary Algorithm, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~490-507

  117. Bandyopadhyay, S., and Mukherjee, A., An Algorithm for Many-Objective Optimization With Reduced Objective Computations: A Study in Differential Evolution, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~400-413

  118. Berenguer, J., and Coello, C., Evolutionary Many-Objective Optimization Based on Kuhn-Munkres’ Algorithm, Evolutionary Multi-Criterion Optimization, 2015, : pp~3-17

  119. Breaban, M., and Iftene, A., Dynamic Objective Sampling in Many-objective Optimization , Procedia Computer Science , 2015, 60: pp~178 - 187

  120. Cai, D., and Yuping, W., A New Uniform Evolutionary Algorithm Based on Decomposition and CDAS for Many-Objective Optimization , Knowledge-Based Systems , 2015, 85: pp~131 - 142

  121. Cai, L., Qu, S., Yuan, Y., and Yao, X., A Clustering-Ranking Method for Many-Objective Optimization , Applied Soft Computing , 2015, 35: pp~681 - 694

  122. Dai, C., and Wang, Y., A New Decomposition Based Evolutionary Algorithm with Uniform Designs for Many-Objective Optimization , Applied Soft Computing , 2015, 30: pp~238 - 248

  123. Dai, C., Wang, Y., and Hu, L., A Uniform Evolutionary Algorithm Based on Decomposition and Contraction for Many-Objective Optimization Problems, Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2, 2015, : pp~167-177

  124. Drozdik, M., Akimoto, Y., Aguirre, H., and Tanaka, K., Computational Cost Reduction of Nondominated Sorting Using the M-Front, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~659-678

  125. Drugan, M.M., Stochastic Pareto Local Search for Many Objective Quadratic Assignment Problem Instances, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~1754-1761

  126. Fernandez, E., Gomez, C., Rivera, G., and Cruz-Reyes, L., Hybrid Metaheuristic Approach for Handling Many Objectives and Decisions on Partial Support in Project Portfolio Optimisation , Information Sciences , 2015, 315: pp~102 - 122

  127. Freire, H., de Moura Oliveira, P.B., Solteiro Pires, E.J., and Bessa, M., Many-Objective Optimization with Corner-Based Search, Memetic Computing, 2015, 7: pp~105-118

  128. de Freitas, A., Fleming, P., and Guimarães, F., Aggregation Trees for Visualization and Dimension Reduction in Many-Objective Optimization , Information Sciences , 2015, 298: pp~288 - 314

  129. Gong, D., Sun, F., Sun, J., and Sun, X., Set-Based Many-Objective Optimization Guided by Preferred Regions, Advanced Intelligent Computing Theories and Applications, 2015, : pp~87-93

  130. Guo, X., Wang, Y., Wang, X., and Wei, J., A New Non-Redundant Objective Set Generation Algorithm in Many-Objective Optimization Problems, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~2851-2858

  131. Hadka, D., Herman, J., Reed, P., and Keller, K., An Open Source Framework for Many-Objective Robust Decision Making, Environmental Modelling and Software, 2015, 74: pp~114-129

  132. Haghighi, A., Analysis of Transient Flow Caused by Fluctuating Consumptions in Pipe Networks: A Many-Objective Genetic Algorithm Approach, Water Resources Management, 2015, 29: pp~2233-2248

  133. Ishibuchi, H., Akedo, N., and Nojima, Y., Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems, Evolutionary Computation, IEEE Transactions on, 2015, 19: pp~264-283

  134. Ishibuchi, H., Masuda, H., and Nojima, Y., Comparing Solution Sets of Different Size in Evolutionary Many-Objective Optimization, Evolutionary Computation (CEC), 2015 IEEE Congress on, 2015, : pp~2859-2866


  135. 2014

  136. Ishibuchi, H., Masuda, H., Tanigaki, Y., and Nojima, Y., Difficulties in Specifying Reference Points to Calculate the Inverted Generational Distance for Many-Objective Optimization Problems, Computational Intelligence in Multi-Criteria Decision-Making (MCDM), 2014 IEEE Symposium on, 2014, : pp~170-177

  137. Ishibuchi, H., Masuda, H., Tanigaki, Y., and Nojima, Y., Review of Coevolutionary Developments of Evolutionary Multi-Objective and Many-Objective Algorithms and Test Problems, Computational Intelligence in Multi-Criteria Decision-Making (MCDM), 2014 IEEE Symposium on, 2014, : pp~178-184

  138. Jaimes, A., and Coello, C., Including Preferences into a Multiobjective Evolutionary Algorithm to Deal with Many-Objective Engineering Optimization Problems , Information Sciences, 2014, 277: pp~1 - 20

  139. Jain, H., and Deb, K., An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach, Evolutionary Computation, IEEE Transactions on, 2014, 18: pp~602-622

  140. Li, B., Li, J., Tang, K., and Yao, X., An Improved Two Archive Algorithm for Many-Objective optimization, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~2869-2876

  141. Li, M., Yang, S., and Liu, X., Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2014, 18: pp~348-365

  142. Li, M., Yang, S., and Liu, X., Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization, IEEE Transactions on Cybernetics, 2014, 44: pp~2568-2584

  143. von Luecken, C., Baran, B., and Brizuela, C., A Survey on Multi-Objective Evolutionary Algorithms for Many-Objective Problems, Computational Optimization and Applications, 2014, 58: pp~707-756

  144. Luo, C., Shimoyama, K., and Obayashi, S., Kriging Model Based Many-Objective Optimization with Efficient Calculation of Expected Hypervolume Improvement, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~1187-1194

  145. Masuda, H., Nojima, Y., and Ishibuchi, H., Visual Examination of the Behavior of EMO Algorithms for Many-Objective Optimization with Many Decision Variables, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~2633-2640

  146. Mohammadi, A., Omidvar, M.N., Li, X., and Deb, K., Integrating User Preferences and Decomposition Methods for Many-Objective Optimization, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~421-428

  147. Reed, P. M., and Hadka, D., Evolving many-objective water management to exploit exascale computing, Water Resources Research, 2014, 50: pp~8367--8373

  148. Sato, H., Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization, Simulated Evolution and Learning, 2014, : pp~274-286

  149. Shang, R., Zhang, K., Jiao, L., Fang, W., Zhang, X., and Tian, X., A Novel Algorithm for Many-Objective Dimension Reductions: Pareto-{PCA}-NSGA-II, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~1974-1981

  150. Sun, X., Xu, R., Zhang, Y., and Gong, D., Sets Evolution-Based Particle Swarm Optimization for Many-Objective Problems, Information and Automation (ICIA), 2014 IEEE International Conference on, 2014, : pp~1119-1124

  151. Sun, X.-Y., Chen, X.-Z., Xu, R.-D., and Gong, D.-W., Hybrid Many-Objective Particle Swarm Optimization Set-Evolution, Intelligent Control and Automation (WCICA), 2014 11th World Congress on, 2014, : pp~1324-1329

  152. Tanigaki, Y., Narukawa, K., Nojima, Y., and Ishibuch, H., Preference-Based NSGA-II for Many-Objective Knapsack Problems, Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on, 2014, : pp~637-642

  153. Wang, H., and Yao, X., Corner Sort for Pareto-Based Many-Objective Optimization, IEEE Transactions on Cybernetics, 2014, 44: pp~92-102

  154. Watanabe, T., Tatsukawa, T., Jaimes, A., Aono, H., Nonomura, T., Oyama, A., and Fujii, K., Many-Objective Evolutionary Computation for Optimization of Separated-Flow Control Using a DBD Plasma Actuator, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~2849-2854

  155. Aguirre, H., Oyama, A., and Tanaka, K., Distribution Search on Evolutionary Many-Objective Optimization: Selection Mappings and Recombination Rate, Evolution, Complexity and Artificial Life, 2014, : pp~241-259

  156. Britto, A., Mostaghim, S., and Pozo, A., Archive Based Multi-swarm Algorithm for Many-Objective Problems, Intelligent Systems (BRACIS), 2014 Brazilian Conference on, 2014, : pp~79-84

  157. Changhong, X., Yong, Z., Dunwei, G., and Xiaoyan, S., Quantum Particle Swarm Algorithm for Many-Objective Optimization Problem, Intelligent Control and Automation (WCICA), 2014 11th World Congress on, 2014, : pp~4566-4571

  158. Chen, S., and Chiang, T., Evolutionary Many-Objective Optimization by MO-NSGA-II with Enhanced Mating Selection, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~1397-1404

  159. Cheung, Y., and Gu, F., Online Objective Reduction for Many-Objective Optimization Problems, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~1165-1171

  160. Curry, D., and Dagli, C., Computational Complexity Measures for Many-objective Optimization Problems , Procedia Computer Science , 2014, 36: pp~185 - 191

  161. Dai, C., Wang, Y., and Ye, M., A New Evolutionary Algorithm Based on Contraction Method for Many-Objective Optimization Problems , Applied Mathematics and Computation , 2014, 245: pp~191 - 205

  162. Deb, K., and Jain, H., An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints, Evolutionary Computation, IEEE Transactions on, 2014, 18: pp~577-601

  163. Denysiuk, R., Costa, L., and Santo, I., Clustering-Based Selection for Evolutionary Many-Objective Optimization, Parallel Problem Solving from Nature – PPSN XIII, 2014, : pp~538-547

  164. Duro, J., Saxena, D., Deb, K., and Zhang, Q., Machine Learning Based Decision Support for Many-Objective Optimization Problems , Neurocomputing , 2014, 146: pp~30 - 47

  165. Everson, R., Walker, D., and Fieldsend, J., Life on the Edge: Characterising the Edges of Mutually Non-Dominating Sets, Evolutionary Computation, 2014, 22: pp~479-501

  166. Freire, H., Oliveira, P., Pires, E., and Bessa, M., Corner Based Many-Objective Optimization, Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), 2014, : pp~125-139

  167. Giagkiozis, I., Purshouse, R., and Fleming, P., Generalized Decomposition and Cross Entropy Methods for Many-Objective Optimization , Information Sciences , 2014, 282: pp~363 - 387

  168. Giuliani, M., Galelli, S., and Soncini-Sessa, R., A Dimensionality Reduction Approach for Many-Objective Markov Decision Processes: Application to a Water Reservoir Operation Problem , Environmental Modelling \& Software , 2014, 57: pp~101 - 114

  169. Giuliani, M., Herman, J. D., Castelletti, A., and Reed, P., Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management, Water Resources Research, 2014, 50: pp~3355--3377

  170. He, Z., and Yen, G.G., Diversity Improvement in Decomposition-Based Multi-Objective Evolutionary Algorithm for Many-Objective Optimization Problems, Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on, 2014, : pp~2409-2414

  171. He, Z., and Yen, G., Comparison of Many-Objective Evolutionary Algorithms Using Performance Metrics Ensemble , Advances in Engineering Software , 2014, 76: pp~1 - 8

  172. Hu, W., Yen, G.G., and Zhang, X., Sensitivity Analysis of Parallel Cell Coordinate System in Many-objective Particle Swarm Optimization, Evolutionary Computation (CEC), 2014 IEEE Congress on, 2014, : pp~2641-2648

  173. Ishibuchi, H., Masuda, H., and Nojima, Y., Selecting a Small Number of Non-Dominated Solutions to be Presented to the Decision Maker, Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on, 2014, : pp~3816-3821


  174. 2013

  175. Ishibuchi, H., Yamane, M., Akedo, N., and Nojima, Y., Many-Objective and Many-Variable Test Problems for Visual Examination of Multi-Objective Search, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~1491-1498

  176. Ishibuchi, H., Yamane, M., and Nojima, Y., Effects of Duplicated Objectives in Many-Objective Optimization Problems on the Search Behavior of Hypervolume-Based Evolutionary Algorithms, Computational Intelligence in Multi-Criteria Decision-Making (MCDM), 2013 IEEE Symposium on, 2013, : pp~25-32

  177. Jaimes, A., Oyama, A., and Fujii, K., Space Trajectory Design: Analysis of a Real-World Many-Objective Optimization Problem, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~2809-2816

  178. Joshi, R., and Deshpande, B., Scalability of Population-Based Search Heuristics for Many-Objective Optimization, Applications of Evolutionary Computation, 2013, : pp~479-488

  179. Kalboussi, S., Bechikh, S., Kessentini, M., and Said, L., On the Influence of the Number of Objectives in Evolutionary Autonomous Software Agent Testing, Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on, 2013, : pp~229-234

  180. Manriquez, A., Pulido, G., Coello, C., and Becerra, R., A Ranking Method Based on the R2 Indicator for Many-Objective Optimization, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~1523-1530

  181. Moen, H., Hansen, N., Hovland, H., and Tørresen, J., Many-Objective Optimization Using Taxi-Cab Surface Evolutionary Algorithm, Evolutionary Multi-Criterion Optimization, 2013, : pp~128-142

  182. Narukawa, K., Effect of Dominance Balance in Many-Objective Optimization, Evolutionary Multi-Criterion Optimization, 2013, : pp~276-290

  183. Pilát, M., and Neruda, R., Aggregate Meta-Models for Evolutionary Multi-Objective and Many-Objective Optimization , Neurocomputing , 2013, 116: pp~392 - 402

  184. Ray, T., Asafuddoula, M., and Isaacs, A., A Steady State Decomposition Based Quantum Genetic Algorithm for Many Objective Optimization, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~2817-2824

  185. Saxena, D.K., Duro, J.A., Tiwari, A., Deb, K., and Zhang, Q., Objective Reduction in Many-Objective Optimization: Linear and Nonlinear Algorithms, Evolutionary Computation, IEEE Transactions on, 2013, 17: pp~77-99

  186. Shimoyama, K., Jeong, S., and Obayashi, S., Kriging-Surrogate-Based Optimization Considering Expected Hypervolume Improvement in Non-Constrained Many-Objective Test Problems, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~658-665

  187. Sinha, A., Saxena, D., Deb, K., and Tiwari, A., Using Objective Reduction and Interactive Procedure to Handle Many-Objective Optimization Problems , Applied Soft Computing , 2013, 13: pp~415 - 427

  188. Tan, Y., Jiao, Y., Li, H., and Wang, X., MOEA/D + Uniform Design: A New Version of MOEA/D for Optimization Problems with Many Objectives , Computers \& Operations Research , 2013, 40: pp~1648 - 1660

  189. Walker, D.J., Everson, R.M., and Fieldsend, J.E., Visualizing Mutually Nondominating Solution Sets in Many-Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2013, 17: pp~165-184

  190. Wang, R., Purshouse, R.C., and Fleming, P.J., Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2013, 17: pp~474-494

  191. Xiao, J., and Wang, K., Ranking-Based Elitist Differential Evolution for Many-Objective Optimization, Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, 2013, : pp~310-313

  192. Yang, S., Li, M., Liu, X., and Zheng, J., A Grid-Based Evolutionary Algorithm for Many-Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2013, 17: pp~721-736

  193. Zhao, H., and Xiao, J., A New Many-Objective Evolutionary Algorithm Based on Self-Adaptive Differential Evolution, Natural Computation (ICNC), 2013 Ninth International Conference on, 2013, : pp~601-605

  194. Aguirre, H., Liefooghe, A., Verel, S., and Tanaka, K., Effects of Population Size on Selection and Scalability in Evolutionary Many-Objective Optimization, Learning and Intelligent Optimization, 2013, : pp~450-454

  195. Aguirre, H., Liefooghe, A., Verel, S., and Tanaka, K., A Study on Population Size and Selection Lapse in Many-Objective Optimization, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~1507-1514

  196. Aguirre, H., Oyama, A., and Tanaka, K., Adaptive ε-Sampling and ε-Hood for Evolutionary Many-Objective Optimization, Evolutionary Multi-Criterion Optimization, 2013, : pp~322-336

  197. Freitas, A.R.R., Fleming, P.J., and Guimaraes, F.G., A Non-Parametric Harmony-Based Objective Reduction Method for Many-Objective Optimization, Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, 2013, : pp~651-656

  198. Gomez, R., and Coello, C., MOMBI: A New Metaheuristic for Many-Objective Optimization Based on the R2 Indicator, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~2488-2495

  199. Gong, D., Wang, G., and Sun, X., Set-Based Genetic Algorithms for Solving Many-Objective Optimization Problems, Computational Intelligence (UKCI), 2013 13th UK Workshop on, 2013, : pp~96-103

  200. Hadka, D., and Reed, P., Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework, Evolutionary Computation, 2013, 21: pp~231-259

  201. Hirano, H., and Yoshikawa, T., A Study on Two-Step Search Based on {PSO} to Improve Convergence and Diversity for Many-Objective Optimization Problems, Evolutionary Computation (CEC), 2013 IEEE Congress on, 2013, : pp~1854-1859


  202. 2012

  203. Kowatari, N., Oyama, A., Aguirre, H., and Tanaka, K., A Study on Large Population MOEA Using Adaptive ε-Box Dominance and Neighborhood Recombination for Many-Objective Optimization, Learning and Intelligent Optimization, 2012, : pp~86-100

  204. Liu, L., Lu, J., and Yang, S., Many-Objective Optimization of Antenna Arrays Using an Improved Multiple-Single-Objective Pareto Sampling Algorithm, #IEEE_J_AWPL#, 2012, 11: pp~399-402

  205. Nagy, R., Suciu, M., and Dumitrescu, D., Exploring Lorenz Dominance, Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2012 14th International Symposium on, 2012, : pp~254-259

  206. Narukawa, K., and Rodemann, T., Examining the Performance of Evolutionary Many-Objective Optimization Algorithms on a Real-World Application, Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on, 2012, : pp~316-319

  207. Santos, T., Takahashi, R.H.C., and Moreira, G.J.P., A CMA Stochastic Differential Equation Approach for Many-Objective Optimization, Evolutionary Computation (CEC), 2012 IEEE Congress on, 2012, : pp~1-6

  208. Sato, H., Aguirre, H., and Tanaka, K., Roles of CCG Crossover and Mutation in Evolutionary Many-Objective Optimization, World Automation Congress (WAC), 2012, 2012, : pp~1-6

  209. Sato, H., Coello, C.A.C., Aguirre, H.E., and Tanaka, K., Dynamic Control of the Number of Crossed Genes in Evolutionary Many-Objective Optimization, Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on, 2012, : pp~1435-1440

  210. Britto, A., and Pozo, A., I-MOPSO: A Suitable {PSO} Algorithm for Many-Objective Optimization, Neural Networks (SBRN), 2012 Brazilian Symposium on, 2012, : pp~166-171

  211. Britto, A., and Pozo, A., Using Archiving Methods to Control Convergence and Diversity for Many-Objective Problems in Particle Swarm Optimization, Evolutionary Computation (CEC), 2012 IEEE Congress on, 2012, : pp~1-8

  212. de Carvalho, A., and Pozo, A., Measuring the Convergence and Diversity of \{CDAS\} Multi-Objective Particle Swarm Optimization Algorithms: A Study of Many-Objective Problems , Neurocomputing , 2012, 75: pp~43 - 51

  213. Guo, X., Wang, X., Wang, M., and Wang, Y., A New Objective Reduction Algorithm for Many-Objective Problems: Employing Mutual Information and Clustering Algorithm, Computational Intelligence and Security (CIS), 2012 Eighth International Conference on, 2012, : pp~11-16

  214. Hadka, D., and Reed, P., Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization, Evolutionary Computation, 2012, 20: pp~423-452

  215. Hirano, H., and Yoshikawa, T., A Study on Two-Step Search Using Global-Best in {PSO} for Multi-Objective Optimization Problems, Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on, 2012, : pp~1894-1897


  216. 2011

  217. Kollat, J. B., Reed, P. M., and Maxwell, R. M., Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics, Water Resources Research, 2011, 47: pp~n/a--n/a

  218. Pilat, M., and Neruda, R., Improving Many-Objective Optimizers with Aggregate Meta-Models, Hybrid Intelligent Systems (HIS), 2011 11th International Conference on, 2011, : pp~555-560

  219. Sato, H., Aguirre, H., and Tanaka, K., Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization, Learning and Intelligent Optimization, 2011, : pp~91-105

  220. Schutze, O., Lara, A., and Coello, C., On the Influence of the Number of Objectives on the Hardness of a Multi-Objective Optimization Problem, Evolutionary Computation, IEEE Transactions on, 2011, 15: pp~444-455

  221. Singh, H.K., Isaacs, A., and Ray, T., A Pareto Corner Search Evolutionary Algorithm and Dimensionality Reduction in Many-Objective Optimization Problems, Evolutionary Computation, IEEE Transactions on, 2011, 15: pp~539-556

  222. Adra, S.F., and Fleming, P.J., Diversity Management in Evolutionary Many-Objective Optimization, Evolutionary Computation, IEEE Transactions on, 2011, 15: pp~183-195

  223. Bader, J., and Zitzler, E., HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization, Evolutionary Computation, 2011, 19: pp~45-76

  224. Batista, L.S., Campelo, F., Guimaraes, F.G., and Ramirez, J.A., A Comparison of Dominance Criteria in Many-Objective Optimization Problems, Evolutionary Computation (CEC), 2011 IEEE Congress on, 2011, : pp~2359-2366

  225. Fabre, M., Pulido, G., Coello, C., and Tello, E., Effective Ranking + Speciation = Many-Objective Optimization, Evolutionary Computation (CEC), 2011 IEEE Congress on, 2011, : pp~2115-2122

  226. Ishibuchi, H., Akedo, N., Ohyanagi, H., Hitotsuyanagi, Y., and Nojima, Y., Many-Objective Test Problems with Multiple Pareto Optimal Regions in a Decision Space, Computational Intelligence in Multicriteria Decision-Making (MDCM), 2011 IEEE Symposium on, 2011, : pp~113-120

  227. Ishibuchi, H., Akedo, N., Ohyanagi, H., and Nojima, Y., Behavior of EMO Algorithms on Many-Objective Optimization Problems with Correlated Objectives, Evolutionary Computation (CEC), 2011 IEEE Congress on, 2011, : pp~1465-1472


  228. 2010

  229. Justesen, P.D., and Ursem, R.K., Many-Objective Distinct Candidates Optimization using Differential Evolution on Centrifugal Pump Design Problems, Evolutionary Computation (CEC), 2010 IEEE Congress on, 2010, : pp~1-8

  230. Kachroudi, S., and Grossard, M., Average Rank Domination Relation for NSGAII and SMPSO Algorithms for Many-Objective Optimization, Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on, 2010, : pp~19-24

  231. Li, M.and Zheng, J., Li, K., Yuan, Q., and Shen, R., Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization, Parallel Problem Solving from Nature, PPSN XI, 2010, : pp~647-656

  232. Lygoe, R., Cary, M., and Fleming, P., A Many-Objective Optimisation Decision-Making Process Applied to Automotive Diesel Engine Calibration, Simulated Evolution and Learning, 2010, : pp~638-646

  233. Murata, T., and Taki, A., Examination of the Performance of Objective Reduction Using Correlation-Based Weighted-Sum for Many Objective Knapsack Problems, Hybrid Intelligent Systems (HIS), 2010 10th International Conference on, 2010, : pp~175-180

  234. Otake, S., Yoshikawa, T., and Furuhashi, T., Basic Study on Aggregation of Objective Functions in Many-Objective Optimization Problems, World Automation Congress (WAC), 2010, 2010, : pp~1-6

  235. Pasia, J.M., Aguirre, H., and Tanaka, K., Improving Multi-Objective Random One-Bit Climbers on MNK-Landscapes, Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on, 2010, : pp~496-501

  236. Sato, H., Aguirre, H., and Tanaka, K., Self-Controlling Dominance Area of Solutions in Evolutionary Many-Objective Optimization, Simulated Evolution and Learning, 2010, : pp~455-465

  237. Sato, H., Aguirre, H., and Tanaka, K., Pareto Partial Dominance MOEA and Hybrid Archiving Strategy Included {CD}AS in Many-Objective Optimization, Evolutionary Computation (CEC), 2010 IEEE Congress on, 2010, : pp~1-8

  238. Sato, H., Aguirre, H., and Tanaka, K., A Study on Interval to Swtich Combination of Objectives Considered in Pareto Partial Dominance MOEA, World Automation Congress (WAC), 2010, 2010, : pp~1-6

  239. Wickramasinghe, U.K., Carrese, R., and Li, X., Designing Airfoils Using a Reference Point Based Evolutionary Many-Objective Particle Swarm Optimization Algorithm, Evolutionary Computation (CEC), 2010 IEEE Congress on, 2010, : pp~1-8

  240. de Carvalho, A.B., and Pozo, A., Analyzing the Control of Dominance Area of Solutions in Particle Swarm Optimization for Many-Objective, Hybrid Intelligent Systems (HIS), 2010 10th International Conference on, 2010, : pp~103-108

  241. de Carvalho, A.B., and Pozo, A., The Control of Dominance Area in Particle Swarm Optimization Algorithms for Many-Objective Problems, Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on, 2010, : pp~140-145

  242. Fabre, M., Pulido, G., and Coello, C., Alternative Fitness Assignment Methods for Many-Objective Optimization Problems, Artifical Evolution, 2010, : pp~146-157

  243. Fabre, M., Pulido, G., and Coello, C., Two Novel Approaches for Many-Objective Optimization, Evolutionary Computation (CEC), 2010 IEEE Congress on, 2010, : pp~1-8

  244. Inoue, M., and Takagi, H., Proposal of F-F-Objective Optimization for Many Objectives and Its Evaluation with a 0/1 Knapsack Problem, Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on, 2010, : pp~520-525


  245. 2009

  246. Ishibuchi, H., Sakane, Y., Tsukamoto, N., and Nojima, Y., Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations, Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on, 2009, : pp~1758-1763

  247. Ishibuchi, H., Sakane, Y., Tsukamoto, N., and Nojima, Y., Effects of Using Two Neighborhood Structures on the Performance of Cellular Evolutionary Algorithms for Many-Objective Optimization, Evolutionary Computation, 2009. CEC '09. IEEE Congress on, 2009, : pp~2508-2515

  248. Ishibuchi, H., Tsukamoto, N., Sakane, Y., and Nojima, Y., Hypervolume Approximation Using Achievement Scalarizing Functions for Evolutionary Many-Objective Optimization, Evolutionary Computation, 2009. CEC '09. IEEE Congress on, 2009, : pp~530-537

  249. Kasprzyk, J. R., Reed, P. M., Kirsch, B. R., and Characklis, G. W., Managing population and drought risks using many-objective water portfolio planning under uncertainty, Water Resources Research, 2009, 45: pp~n/a--n/a

  250. Saxena, D.K., Ray, T., Deb, K., and Tiwari, A., Constrained Many-Objective Optimization: A Way Forward, Evolutionary Computation, 2009. CEC '09. IEEE Congress on, 2009, : pp~545-552

  251. Adra, S., and Fleming, P., A Diversity Management Operator for Evolutionary Many-Objective Optimisation, Evolutionary Multi-Criterion Optimization, 2009, : pp~81-94


  252. 2008

  253. Ishibuchi, H., Tsukamoto, N., and Nojima, Y., Behavior of Evolutionary Many-Objective Optimization, Computer Modeling and Simulation, 2008. UKSIM 2008. Tenth International Conference on, 2008, : pp~266-271

  254. Ishibuchi, H., Tsukamoto, N., and Nojima, Y., Evolutionary Many-Objective Optimization: A Short Review, Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, : pp~2419-2426

  255. Ishibuchi, H., Tsukamoto, N., and Nojima, Y., Evolutionary Many-Objective Optimization, Genetic and Evolving Systems, 2008. GEFS 2008. 3rd International Workshop on, 2008, : pp~47-52

  256. Kang, Z., Kang, L., Li, C., Chen, Y., and Liu, M., Convergence Properties of E-Optimality Algorithms for Many Objective Optimization Problems, Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, 2008, : pp~472-477

  257. Zou, X., Chen, Y., Liu, M., and Kang, L., A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems, #IEEE_J_SMCB#, 2008, 38: pp~1402-1412

  258. Hughes, E., Many Objective Optimisation: Direct Objective Boundary Identification, Parallel Problem Solving from Nature – PPSN X, 2008, : pp~733-742


  259. 2007

  260. Köppen, M., and Yoshida, K., Substitute Distance Assignments in NSGA-II for Handling Many-objective Optimization Problems, Evolutionary Multi-Criterion Optimization, 2007, : pp~727-741

  261. Kang, Z., Kang, L., Zou, X., Liu, M., Li, C., Yang, M., Li, Y., Chen, Y., and Zeng, S., A New Evolutionary Decision Theory for Many-Objective Optimization Problems, Advances in Computation and Intelligence, 2007, : pp~1-11

  262. Kukkonen, S., and Lampinen, J., Ranking-Dominance and Many-Objective Optimization, Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007, : pp~3983-3990

  263. Purshouse, R.C., and Fleming, P.J., On the Evolutionary Optimization of Many Conflicting Objectives, Evolutionary Computation, IEEE Transactions on, 2007, 11: pp~770-784

  264. Wang, G., and Jiang, H., Fuzzy-Dominance and Its Application in Evolutionary Many Objective Optimization, Computational Intelligence and Security Workshops, 2007. CISW 2007. International Conference on, 2007, : pp~195-198

  265. Wang, G., and Wu, J., A New Fuzzy Dominance GA Applied to Solve Many-Objective Optimization Problem, Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on, 2007, : pp~


  266. 2005

  267. Hughes, E.J., Evolutionary Many-Objective Optimisation: Many Once or One Many?, Evolutionary Computation, 2005. The 2005 IEEE Congress on, 2005, : pp~222-227


  268. 2004

  269. Farina, M., and Amato, P., A Fuzzy Definition of Optimality for Many-Criteria Optimization Problems, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2004, 34: pp~315-326


  270. 2003

  271. Purshouse, R.C., and Fleming, P.J., Evolutionary Many-Objective Optimisation: An Exploratory Analysis, Evolutionary Computation, 2003. CEC '03. The 2003 Congress on, 2003, : pp~2066-2073