Open Access
Volume 37, Number 5, October 2019
Page(s) 1000 - 1010
Published online 14 January 2020
  1. MohamedA WSabryH Z. Constrained Optimization Based on Modified Differential Evolution Algorithm[J]. Information Sciences, 2012, 194(5): 171–208 [Article] [CrossRef] [Google Scholar]
  2. StornR, PriceK. Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces[J]. Journal of Global Optimization, 1997, 11(4): 341–359 [Article] [CrossRef] [Google Scholar]
  3. Takahama T, SAKAI S. Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites[C]//IEEE Congress on Evolutionary Computation, 2006: 1–8 [Google Scholar]
  4. MallipeddiR, SuganthanP N. Ensemble of Constraint Handling Techniques[J]. IEEE Trans on Evolutionary Computation, 2010, 14(4): 561–579 [Article] [CrossRef] [Google Scholar]
  5. WangY, CaiZ. Constrained Evolutionary Optimization by Means of (μ+λ)-Differential Evolution and Improved Adaptive Trade-Off Model[J]. Evolutionary Computation, 2014, 19(2): 249–285 [CrossRef] [Google Scholar]
  6. JiaG, WangY, CaiZ, et al. An Improved (μ+λ)-Constrained Differential Evolution for Constrained Optimization[J]. Information Sciences, 2013, 222(4): 302–322 [Article] [CrossRef] [Google Scholar]
  7. GongW, CaiZ, LiangD. Engineering Optimization by Means of an Improved Constrained Differential Evolution[J]. Comput Methods Appl Mech Engrg, 2014, 268: 884–904 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  8. WangH, RahnamayanS, WuZ J. Parallel Differential Evolution with Self Adapting Control Parameters and Generalized Opposition-Based Learning for Solving High-Dlimensional Optimization Problems[J]. Journal of Parallel and Distributed Computing, 2013, 73(1): 62–73 [Article] [CrossRef] [Google Scholar]
  9. Tizhoosh H R. Opposition-Based Learning: a New Scheme for Machine Intelligence[C]//The IEEE International Conference of Intelligent for Modeling, Control and Automation, 2005: 695–701 [Google Scholar]
  10. WangH, Wu Z J, RahnamayanS, et al. Enhancing Particle Swarm Optimization Using Generalized Opposition-Based Learning[J]. Information Sciences, 2011, 1814699–4714 [Article] [CrossRef] [Google Scholar]
  11. Rahnamayan S, TizhooshH R, SalamaM M A. Opposition Versus Randomness in Soft Computing Techniques[J]. Soft Comput, 2008, 8(2): 906–918 [Article] [CrossRef] [Google Scholar]
  12. WangYong, Cai Zixing, ZhouYuren. An Adaptive Trade-Off Model for Constrained Evolutionary Optimization[J]. IEEE Trans on Evolutionary Computation, 2008, 12(1): 80–92 [Article] [CrossRef] [Google Scholar]
  13. GongW, CaiZ, LiangD. Adaptive Ranking Mutation Operator Based Differential Evolution for Constrained Optimization[J]. IEEE Trans on Cybernetics, 2015, 45(4): 716–727 [Article] [CrossRef] [Google Scholar]
  14. Elsayed S M, Sarker R A, Essam D L. Multi-Operator Based Evolutionary Algorithms for Solving Constrained Optimization Problems[M]. Computers and Operations Research, 2011, 38(2): 1877–1896 [CrossRef] [Google Scholar]
  15. HeDahai, LiYuanxiang, Gong Wen, et al. An Adaptive DifferentiaI EvoIution AIgorithm for Constrained Optimization ProbIems[J]. Acta Electronica Sinica, 2016, 44(10): 2535–2542 [Article] [Google Scholar]
  16. BecerraR L, CoelloC A C. Cultured Differential Evolution for Constrained Optimization[J]. Computer Methods in Applied Mechanics & Engineering, 2006, 195(33/34/35/364303–4322 [Article] [CrossRef] [Google Scholar]
  17. Efrén Mezura-Montes, Coello C A C. Promising Infeasibility and Multiple Offspring Incorporated to Differential Evolution for Constrained Optimization[C]//Proceedings of Genetic and Evolutionary Computation, Washington DC, USA, 2005: 225–232 [Google Scholar]
  18. Mezuramontes E, Palomequeortiz A G. Self-Adaptive and Deterministic Parameter Control in Differential Evolution for Constrained Optimization[J]. 2009, 198: 95–120 [Google Scholar]
  19. ZhengJianguo, Wang Xiang, Liu Ronghui, et al. Dlif Terential Evolution Algorithm for Constrained Optimization Problems[J]. Journal of Software, 2012, 23(9): 2374–2387 [Article] [CrossRef] [Google Scholar]
  20. GaoW F, YenG G, LiuS Y. A Dual-Population Differential Evolution with Coevolution for Constrained Optimization[J]. IEEE Trans on Cybernetics, 2015, 45(5): 1094–1107 [Article] [Google Scholar]

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