.752 工程科学学报，第43卷.第6期 [35]Li L,Wang WL,Li W K,et al.A novel ranking-based optimal [50]Liu T Y,Jiao L C,Ma W P,et al.Cultural quantum-behaved guides selection strategy in MOPSO.Procedia Comput Sci,2016, particle swarm optimization for environmental/economic dispatch. 91:1001 Appl Soft Comput,2016,48:597 [36]Tang B W,Zhu Z X,Shin H S,et al.A framework for multi- [51]Pan A Q,Wang L,Guo W A,et al.A diversity enhanced objective optimisation based on a new self-adaptive particle swarm multiobjective particle swarm optimization.Inf Sci,2018,436-437: optimisation algorithm.Inf Sci,2017,420:364 441 [37]Yang S X,Li M Q,Liu X H,et al.A grid-based evolutionary [52]Li L,Wang W L,Xu X L.Multi-objective particle swarm algorithm for many-objective optimization.IEEE Trans Evol optimization based on global margin ranking.Inf Sci,2016,375: Comput,2013,17(5:721 30 [38]Feng Q,Li Q,Chen P,et al.Multiobjective particle swarm [53]Cheng T L,Chen M Y,Fleming P J,et al.A novel hybrid teaching optimization algorithm based on adaptive angle division.IEEE learning based multi-objective particle swarm optimization 4 ccess,.2019,7:87916 Neurocomputing.2017,222:11 [39]Zhan Z H,LiJJ,Cao JN.et al.Multiple populations for multiple [54]Yu J P,Wang W,Wu G F,et al.Game mechanism based multi- objectives:A coevolutionary technique for solving multiobjective objective particle swarm optimization.Compur Eng Des,2020, optimization problems.IEEE Trans Cybern,2013,43(2):445 41(4):964 [40]Depolli M,Trobec R,Filipic B.Asynchronous master-slave (喻金平，王伟，巫光福，等.基于博弈机制的多目标粒子群优化 parallelization of differential evolution for multi-objective 算法.计算机工程与设计，2020,41(4)：964) optimization.Evol Comput,2013,21(2):261 [55]Zhang X Y,Zheng X T,Cheng R,et al.A competitive mechanism [41]Yang Y C,Zhang T X,Yi W,et al.Deployment of multistatic based multi-objective particle swarm optimizer with fast radar system using multi-objective particle swarm optimisation. convergence.Inf Sci,2018,427:63 IET Radar Sonar Navig,2018,12(5):485 [56]Coello C A C,Pulido G T,Lechuga M S.Handling multiple [42]Luo J G.Qi Y T,Xie J C.et al.A hybrid multi-objective PSO- objectives with particle swarm optimization.IEEE Trans Evol EDA algorithm for reservoir flood control operation.App/Soft Comput,2004,8(3):256 Comput,2015,34:526 [57]Zhan Z H,Zhang J,Li Y,et al.Adaptive particle swarm [43]Yao GS,Ding YS,Jin YC,et al.Endocrine-based coevolutionary optimization.IEEE Trans Syst Man Cybern Part B Cybern,2009. multi-swarm for multi-objective workflow scheduling in a cloud 39(6):1362 system.Sofi Comput,2017,21(15):4309 [58]Peng G,Fang Y W,Chai D,et al.Multi-objective particle swarm [44]Zhang WZ.Li GQ.Zhang WW.et al.A cluster based PSO with optimization algorithm based on sharing-learning and Cauchy leader updating mechanism and ring-topology for multimodal mutation ll Proceedings of the 35th Chinese Control Conference multi-objective optimization.Swarm Evol Comput,2019,50: Chengdu,2016:9155 100569 [59]Zhang W,Huang W M.Multi-strategy adaptive multi-objective [45]Liang J,Guo QQ.Yue C T,et al.A self-organizing multi- particle swarm optimization algorithm based on swarm partition objective particle swarm optimization algorithm for multimodal [J/OL.Acta Autom Sin,(2020-09-16)[2020-10-31].http/ks. multi-objective problems /International Conference on Swarm cnki.net/kems/detail/11.2109.TP.20200915.0941.002.html Intelligence.Shanghai,2018:550 (张伟，黄卫民.基于种群分区的多策略自适应多目标粒子群算 [46]Huang P Q,Liu J C,Tan S B,et al.Application of the hybrid 法[J/0L].自动化学报(2020-09-16)2020-10-31】.http:/kns.cnki. multi-objective particle swarm optimization algorithm in load net/kcms/detail/11.2109.TP.20200915.0941.002.html) distribution of hot finishing mills.Control Theory Appl,2017, [60]Yang J M,Ma MM,Che H J,et al.Multi-objective adaptive 34(1):93 chaotic particle swarm optimization algorithm.Control Decis, (黄佩秋，刘建昌，谭树彬，等.混合多目标粒子群优化算法在热 2015,30(12:2168 精轧负荷分配优化中的应用.控制理论与应用，2017,34(1)： (杨景明，马明明，车海军，等.多目标自适应混沌粒子群优化算 93) 法.控制与决策，2015,30(12)：2168) [47]Dai C,Wang Y P,Ye M.A new multi-objective particle swarm [61]Han M,He Y.Adaptive multi-objective particle swarm optimization algorithm based on decomposition.Inf Sci,2015,325 optimization with Gaussian chaotic mutation and elite learning. 541 Control Decis,2016,31(8):1372 [48]Qi Y T,Ma X L,Liu F,et al.MOEA/D with adaptive weight (韩敏，何泳.基于高斯混沌变异和精英学习的自适应多目标粒 adjustment.Evol Comput,2014,22(2):231 子群算法.控制与决策，2016,31(8)：1372) [49]Albaity H,Meshoul S,Kaban A.On extending quantum behaved [62]Moslemi H,Zandieh M.Comparisons of some improving particle swarm optimization to multiobjective context / strategies on MOPSO for multi-objective(r,Q)inventory system. Proceedings of the 2012 IEEE Congress on Evolutionary Expert Syst4ppl,2011,38(10):12051 Computation,CEC 2012.Brisbane,2012:1 [63]Wang X W,Xue L K,Gu X S.Multi-objective particle swarmLi L, Wang W L, Li W K, et al. A novel ranking-based optimal guides selection strategy in MOPSO. Procedia Comput Sci, 2016, 91: 1001 [35] Tang  B  W,  Zhu  Z  X,  Shin  H  S,  et  al.  A  framework  for  multi￾objective optimisation based on a new self-adaptive particle swarm optimisation algorithm. Inf Sci, 2017, 420: 364 [36] Yang  S  X,  Li  M  Q,  Liu  X  H,  et  al.  A  grid-based  evolutionary algorithm  for  many-objective  optimization. IEEE Trans Evol Comput, 2013, 17（5）: 721 [37] Feng  Q,  Li  Q,  Chen  P,  et  al.  Multiobjective  particle  swarm optimization  algorithm  based  on  adaptive  angle  division. IEEE Access, 2019, 7: 87916 [38] Zhan Z H, Li J J, Cao J N, et al. Multiple populations for multiple objectives: A coevolutionary technique for solving multiobjective optimization problems. IEEE Trans Cybern, 2013, 43（2）: 445 [39] Depolli  M,  Trobec  R,  Filipič  B.  Asynchronous  master-slave parallelization  of  differential  evolution  for  multi-objective optimization. Evol Comput, 2013, 21（2）: 261 [40] Yang  Y  C,  Zhang  T  X,  Yi  W,  et  al.  Deployment  of  multistatic radar  system  using  multi-objective  particle  swarm  optimisation. IET Radar Sonar Navig, 2018, 12（5）: 485 [41] Luo  J  G,  Qi  Y  T,  Xie  J  C,  et  al.  A  hybrid  multi-objective  PSO￾EDA  algorithm  for  reservoir  flood  control  operation. Appl Soft Comput, 2015, 34: 526 [42] Yao G S, Ding Y S, Jin Y C, et al. Endocrine-based coevolutionary multi-swarm  for  multi-objective  workflow  scheduling  in  a  cloud system. Soft Comput, 2017, 21（15）: 4309 [43] Zhang W Z, Li G Q, Zhang W W, et al. A cluster based PSO with leader  updating  mechanism  and  ring-topology  for  multimodal multi-objective  optimization. Swarm Evol Comput,  2019,  50: 100569 [44] Liang  J,  Guo  Q  Q,  Yue  C  T,  et  al.  A  self-organizing  multi￾objective  particle  swarm  optimization  algorithm  for  multimodal multi-objective  problems  // International Conference on Swarm Intelligence. Shanghai, 2018: 550 [45] Huang  P  Q,  Liu  J  C,  Tan  S  B,  et  al.  Application  of  the  hybrid multi-objective  particle  swarm  optimization  algorithm  in  load distribution  of  hot  finishing  mills. Control Theory Appl,  2017, 34（1）: 93 （黄佩秋, 刘建昌, 谭树彬, 等. 混合多目标粒子群优化算法在热 精轧负荷分配优化中的应用. 控制理论与应用, 2017, 34（1）： 93） [46] Dai  C,  Wang  Y  P,  Ye  M.  A  new  multi-objective  particle  swarm optimization algorithm based on decomposition. Inf Sci, 2015, 325: 541 [47] Qi  Y  T,  Ma  X  L,  Liu  F,  et  al.  MOEA/D  with  adaptive  weight adjustment. Evol Comput, 2014, 22（2）: 231 [48] Albaity H, Meshoul S, Kaban A. On extending quantum behaved particle  swarm  optimization  to  multiobjective  context  // Proceedings of the 2012 IEEE Congress on Evolutionary Computation, CEC 2012. Brisbane, 2012: 1 [49] Liu  T  Y,  Jiao  L  C,  Ma  W  P,  et  al.  Cultural  quantum-behaved particle swarm optimization for environmental/economic dispatch. Appl Soft Comput, 2016, 48: 597 [50] Pan  A  Q,  Wang  L,  Guo  W  A,  et  al.  A  diversity  enhanced multiobjective particle swarm optimization. Inf Sci, 2018, 436-437: 441 [51] Li  L,  Wang  W  L,  Xu  X  L.  Multi-objective  particle  swarm optimization  based  on  global  margin  ranking. Inf Sci,  2016,  375: 30 [52] Cheng T L, Chen M Y, Fleming P J, et al. A novel hybrid teaching learning  based  multi-objective  particle  swarm  optimization. Neurocomputing, 2017, 222: 11 [53] Yu J P, Wang W, Wu G F, et al. Game mechanism based multi￾objective  particle  swarm  optimization. Comput Eng Des,  2020, 41（4）: 964 （喻金平, 王伟, 巫光福, 等. 基于博弈机制的多目标粒子群优化 算法. 计算机工程与设计, 2020, 41（4）：964） [54] Zhang X Y, Zheng X T, Cheng R, et al. A competitive mechanism based  multi-objective  particle  swarm  optimizer  with  fast convergence. Inf Sci, 2018, 427: 63 [55] Coello  C  A  C,  Pulido  G  T,  Lechuga  M  S.  Handling  multiple objectives  with  particle  swarm  optimization. IEEE Trans Evol Comput, 2004, 8（3）: 256 [56] Zhan  Z  H,  Zhang  J,  Li  Y,  et  al.  Adaptive  particle  swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern, 2009, 39（6）: 1362 [57] Peng G, Fang Y W, Chai D, et al. Multi-objective particle swarm optimization  algorithm  based  on  sharing-learning  and  Cauchy mutation // Proceedings of the 35th Chinese Control Conference. Chengdu, 2016: 9155 [58] Zhang  W,  Huang  W  M.  Multi-strategy  adaptive  multi-objective particle  swarm  optimization  algorithm  based  on  swarm  partition [J/OL]. Acta Autom Sin,  (2020-09-16)  [2020-10-31]. http://kns. cnki.net/kcms/detail/11.2109.TP.20200915.0941.002.html （ 张伟, 黄卫民. 基于种群分区的多策略自适应多目标粒子群算 法[J/OL]. 自动化学报(2020-09-16) [2020-10-31]. http://kns.cnki. net/kcms/detail/11.2109.TP.20200915.0941.002.html） [59] Yang  J  M,  Ma  M  M,  Che  H  J,  et  al.  Multi-objective  adaptive chaotic  particle  swarm  optimization  algorithm. Control Decis, 2015, 30（12）: 2168 （杨景明, 马明明, 车海军, 等. 多目标自适应混沌粒子群优化算 法. 控制与决策, 2015, 30（12）：2168） [60] Han  M,  He  Y.  Adaptive  multi-objective  particle  swarm optimization  with  Gaussian  chaotic  mutation  and  elite  learning. Control Decis, 2016, 31（8）: 1372 （韩敏, 何泳. 基于高斯混沌变异和精英学习的自适应多目标粒 子群算法. 控制与决策, 2016, 31（8）：1372） [61] Moslemi  H,  Zandieh  M.  Comparisons  of  some  improving strategies on MOPSO for multi-objective (r, Q) inventory system. Expert Syst Appl, 2011, 38（10）: 12051 [62] [63] Wang  X  W,  Xue  L  K,  Gu  X  S.  Multi-objective  particle  swarm · 752 · 工程科学学报，第 43 卷，第 6 期