冯茜等:多目标粒子群优化算法研究综述 ·753· optimization algorithm based on three status coordinating [77]Hu W,Yen GG,Luo G C.Many-objective particle swarm searching.Control Decis,2015,30(11):1945 optimization using two-stage strategy and parallel cell coordinate (王学武,薛立卡,顾幸生.三态协调搜索多目标粒子群优化算 system.IEEE Trans Cybern,2017,47(6):1446 法.控制与决策,2015,30(11):1945) [78]Meza J,Espitia H,Montenegro C,et al.MOVPSO:vortex multi- [64]Peng G,Fang Y W.Peng W S,et al.Multi-objective particle objective particle swarm optimization.Appl Soft Comput,2017, optimization algorithm based on sharing-learning and dynamic 52:1042 crowding distance.Optik,2016,127(12):5013 [79]Pan A Q,Tian H J,Wang L,et al.A decomposition-based unified [65]Li J Z,Chen W N,Zhang J,et al.A parallel implementation of evolutionary algorithm for many-objective problems using particle multiobjective particle swarm optimization algorithm based on swarm optimization.Math Problems Eng,2016,2016:6761545 decomposition /l Proceedings of2015 IEEE Symposium Series on [80]Liu X F,Zhan Z H,Gao Y,et al.Coevolutionary particle swarm Computational Intelligence.Cape Town,2015:1310 optimization with bottleneck objective learning strategy for many. [66]Xu G,Yang Y Q,Liu BB,et al.An efficient hybrid multi- objective optimization.IEEE Trans Evol Comput,2019,23(4): objective particle swarm optimization with a multi-objective 587 dichotomy line search.JComput Appl Math,2015,280:310 [81]Aleti A,Moser I.A systematic literature review of adaptive [67]Cheng S X,Zhan H,Shu Z X.An innovative hybrid multi- parameter control methods for evolutionary algorithms.ACM objective particle swarm optimization with or without constraints Comput Surv,2016,49(3):56 handling.Appl Sofi Compul,2016,47:370 [82]Han H G,Lu W,Qiao J F.An adaptive multiobjective particle [68]Yu H,Wang Y J,Chen Q,et al.Multi-objective particle swarm swarm optimization based on multiple adaptive methods.IEEE optimization based on multi-population dynamic cooperation. Trans Cybern,,2017,47(9:2754 Electron Sci Technol,2019,32(10):28 [83]Xia L R,Li R X,Liu Q Y,et al.An adaptive multi-objective (于慧,王宇嘉,陈强,等.基于多种群动态协同的多目标粒子群 particle swarm optimization algorithm based dynamic AHP and its 算法.电子科技,2019,32(10):28) application.Control Decis,2015,30(2):215 [69]Liu R C,Li J X,Fan J,et al.A coevolutionary technique based on (夏立荣,李润学,刘启玉,等.基于动态层次分析的自适应多目 multi-swarm particle swarm optimization for dynamic multi 标粒子群优化算法及其应用.控制与决策,2015,30(2):215) objective optimization.EurJOper Res,2017,261(3):1028 [70]Han HG.Lu W,Zhang L,et al.Adaptive gradient multiobjective [84]Liu Y X,Lu H,Cheng S,et al.An adaptive online parameter particle swarm optimization.IEEE Trans Cybern,2018,48(11): control algorithm for particle swarm optimization based on 3067 reinforcement leaming I Proceedings of the 2019 IEEE Congress [71]Lin QZ,Liu S B,Zhu QL,et al.Particle swarm optimization with on Evolutionary Computation,CEC 2019.Wellington,2019:815 a balanceable fitness estimation for many-objective optimization [85]Hu M Q,Wu T,Weir J D.An adaptive particle swarm problems.IEEE Trans Evol Comput,2018,22(1):32 optimization with multiple adaptive methods.IEEE Trans Evol [72]Hu W,Yen GG.Adaptive multiobjective particle swarm Comput,2013,17(5):705 optimization based on parallel cell coordinate system.IEEE Trans [86]Palafox L,Noman N,Iba H.Reverse engineering of gene Evol Compt,2015,19(1):1 regulatory networks using dissipative particle swarm optimization. [73]Deb K,Jain H.An evolutionary many-objective optimization IEEE Trans Evol Comput,2013,17(4):577 algorithm using reference-point-based nondominated sorting [87]Ding S X,Chen C,Xin B,et al.A bi-objective load balancing approach,Part I:Solving problems with box constraints.IEEE model in a distributed simulation system using NSGA-II and Trans Evol Comput,2014,18(4):577 MOPSO approaches.Appl Soft Comput,2018,63:249 [74]Wu B L,Hu W,Hu J J,et al.Adaptive multiobjective particle [88]Yue C T,Qu B Y,Liang J.A multi-objective particle swarm swarm optimization based on evolutionary state estimation.IEEE optimizer using ring topology for solving multimodal multi- Trans Cybern,2019 objective problems.IEEE Trans Evol Comput,2018,22(5):805 [75]Figueiredo E M N,Ludermir T B,Bastos-Filho C J A.Many [89]Gao H J,Pan D Z.A multi-objective particle swarm optimization objective particle swarm optimization.InfSci,2016,374:115 algorithm with star structure to solve the multi-modal multi- [76]Lin Q Z,Li J Q,Du Z H,et al.A novel multi-objective particle objective problem.Comput Eng Sci,2020,42(8):1472 swarm optimization with multiple search strategies.Eur/Oper (高海军,潘大志.星型结构的多目标粒子群算法求解多模态多 Res,2015,247(3):732 目标问题.计算机工程与科学,2020,42(8):1472)optimization algorithm based on three status coordinating searching. Control Decis, 2015, 30(11): 1945 (王学武, 薛立卡, 顾幸生. 三态协调搜索多目标粒子群优化算 法. 控制与决策, 2015, 30(11):1945) Peng G, Fang Y W, Peng W S, et al. Multi-objective particle optimization algorithm based on sharing-learning and dynamic crowding distance. Optik, 2016, 127(12): 5013 [64] Li J Z, Chen W N, Zhang J, et al. A parallel implementation of multiobjective particle swarm optimization algorithm based on decomposition // Proceedings of 2015 IEEE Symposium Series on Computational Intelligence. Cape Town, 2015: 1310 [65] Xu G, Yang Y Q, Liu B B, et al. An efficient hybrid multiobjective particle swarm optimization with a multi-objective dichotomy line search. J Comput Appl Math, 2015, 280: 310 [66] Cheng S X, Zhan H, Shu Z X. An innovative hybrid multiobjective particle swarm optimization with or without constraints handling. Appl Soft Comput, 2016, 47: 370 [67] Yu H, Wang Y J, Chen Q, et al. Multi-objective particle swarm optimization based on multi-population dynamic cooperation. Electron Sci Technol, 2019, 32(10): 28 (于慧, 王宇嘉, 陈强, 等. 基于多种群动态协同的多目标粒子群 算法. 电子科技, 2019, 32(10):28) [68] Liu R C, Li J X, Fan J, et al. A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multiobjective optimization. Eur J Oper Res, 2017, 261(3): 1028 [69] Han H G, Lu W, Zhang L, et al. Adaptive gradient multiobjective particle swarm optimization. IEEE Trans Cybern, 2018, 48(11): 3067 [70] Lin Q Z, Liu S B, Zhu Q L, et al. Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems. IEEE Trans Evol Comput, 2018, 22(1): 32 [71] Hu W, Yen G G. Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system. IEEE Trans Evol Comput, 2015, 19(1): 1 [72] Deb K, Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Trans Evol Comput, 2014, 18(4): 577 [73] Wu B L, Hu W, Hu J J, et al. Adaptive multiobjective particle swarm optimization based on evolutionary state estimation. IEEE Trans Cybern, 2019 [74] Figueiredo E M N, Ludermir T B, Bastos-Filho C J A. Many objective particle swarm optimization. Inf Sci, 2016, 374: 115 [75] Lin Q Z, Li J Q, Du Z H, et al. A novel multi-objective particle swarm optimization with multiple search strategies. Eur J Oper Res, 2015, 247(3): 732 [76] Hu W, Yen G G, Luo G C. Many-objective particle swarm optimization using two-stage strategy and parallel cell coordinate system. IEEE Trans Cybern, 2017, 47(6): 1446 [77] Meza J, Espitia H, Montenegro C, et al. MOVPSO: vortex multiobjective particle swarm optimization. Appl Soft Comput, 2017, 52: 1042 [78] Pan A Q, Tian H J, Wang L, et al. A decomposition-based unified evolutionary algorithm for many-objective problems using particle swarm optimization. Math Problems Eng, 2016, 2016: 6761545 [79] Liu X F, Zhan Z H, Gao Y, et al. Coevolutionary particle swarm optimization with bottleneck objective learning strategy for manyobjective optimization. IEEE Trans Evol Comput, 2019, 23(4): 587 [80] Aleti A, Moser I. A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Comput Surv, 2016, 49(3): 56 [81] Han H G, Lu W, Qiao J F. An adaptive multiobjective particle swarm optimization based on multiple adaptive methods. IEEE Trans Cybern, 2017, 47(9): 2754 [82] Xia L R, Li R X, Liu Q Y, et al. An adaptive multi-objective particle swarm optimization algorithm based dynamic AHP and its application. Control Decis, 2015, 30(2): 215 (夏立荣, 李润学, 刘启玉, 等. 基于动态层次分析的自适应多目 标粒子群优化算法及其应用. 控制与决策, 2015, 30(2):215) [83] Liu Y X, Lu H, Cheng S, et al. An adaptive online parameter control algorithm for particle swarm optimization based on reinforcement learning // Proceedings of the 2019 IEEE Congress on Evolutionary Computation, CEC 2019. Wellington, 2019: 815 [84] Hu M Q, Wu T, Weir J D. An adaptive particle swarm optimization with multiple adaptive methods. IEEE Trans Evol Comput, 2013, 17(5): 705 [85] Palafox L, Noman N, Iba H. Reverse engineering of gene regulatory networks using dissipative particle swarm optimization. IEEE Trans Evol Comput, 2013, 17(4): 577 [86] Ding S X, Chen C, Xin B, et al. A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches. Appl Soft Comput, 2018, 63: 249 [87] Yue C T, Qu B Y, Liang J. A multi-objective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Trans Evol Comput, 2018, 22(5): 805 [88] Gao H J, Pan D Z. A multi-objective particle swarm optimization algorithm with star structure to solve the multi-modal multiobjective problem. Comput Eng Sci, 2020, 42(8): 1472 (高海军, 潘大志. 星型结构的多目标粒子群算法求解多模态多 目标问题. 计算机工程与科学, 2020, 42(8):1472) [89] 冯 茜等: 多目标粒子群优化算法研究综述 · 753 ·