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                 [13]   Yang  SX,  Li  MQ,  Liu  XH,  Zheng  JH.  A  grid-based  evolutionary  algorithm  for  many-objective  optimization.  IEEE  Trans.  on
                     Evolutionary Computation, 2013, 17(5): 721–736. [doi: 10.1109/TEVC.2012.2227145]
                 [14]   Cai XJ, Hu ZM, Zhang ZX, Wang Q, Cui ZH, Zhang WS. Multi-UAV coordinated path planning based on many-objective optimization.
                     SCIENTIA SINICA Informationis, 2021, 51(6): 985–996 (in Chinese with English abstract). [doi: 10.1360/SSI-2020-0218]
                 [15]   Zhang XY, Tian Y, Jin YC. A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans. on Evolutionary
                     Computation, 2015, 19(6): 761–776. [doi: 10.1109/TEVC.2014.2378512]
                 [16]   Li BD, Tang K, Li JL, Yao X. Stochastic ranking algorithm for many-objective optimization based on multiple indicators. IEEE Trans. on
                     Evolutionary Computation, 2016, 20(6): 924–938. [doi: 10.1109/TEVC.2016.2549267]
                 [17]   Praditwong K, Yao X. A new multi-objective evolutionary optimisation algorithm: The two-archive algorithm. In: Proc. of the 2007 Int’l
                     Conf. on Computational Intelligence and Security. Guangzhou: Springer, 2007. 95–104. [doi: 10.1007/978-3-540-74377-4_11]
                 [18]   Pan  LQ,  Lin  JQ,  Wang  HD,  He  C,  Tan  KC,  Jin  YC.  Computationally  expensive  high-dimensional  multiobjective  optimization  via
                     surrogate-assisted reformulation and decomposition. IEEE Trans. on Evolutionary Computation, 2025, 29(4): 921–935. [doi: 10.1109/
                     TEVC.2024.3380327]
                 [19]   Yuan Y, Xu H, Wang B, Yao X. A new dominance relation-based evolutionary algorithm for many-objective optimization. IEEE Trans.
                     on Evolutionary Computation, 2016, 20(1): 16–37. [doi: 10.1109/TEVC.2015.2420112]
                 [20]   Tian Y, Cheng R, Zhang XY, Su YS, Jin YC. A strengthened dominance relation considering convergence and diversity for evolutionary
                     many-objective optimization. IEEE Trans. on Evolutionary Computation, 2019, 23(2): 331–345. [doi: 10.1109/TEVC.2018.2866854]
                 [21]   Zhu  CW,  Xu  LH,  Goodman  ED.  Generalization  of  Pareto-optimality  for  many-objective  evolutionary  optimization.  IEEE  Trans.  on
                     Evolutionary Computation, 2016, 20(2): 299–315. [doi: 10.1109/TEVC.2015.2457245]
                 [22]   Lu  X,  Tan  YY,  Zheng  W,  Meng  LL.  A  decomposition  method  based  on  random  objective  division  for  MOEA/D  in  many-objective
                     optimization. IEEE Access, 2020, 8: 103550–103564. [doi: 10.1109/ACCESS.2020.2999417]
                 [23]   Farias LRC, Araújo AFR. IM-MOEA/D: An inverse modeling multi-objective evolutionary algorithm based on decomposition. In: Proc.
                     of the 2021 IEEE Int’l Conf. on Systems, Man, and Cybernetics (SMC). Melbourne: IEEE, 2021. 462–467. [doi: 10.1109/SMC52423.
                     2021.9658650]
                 [24]   Sun  YA,  Yen  GG,  Yi  Z.  IGD  indicator-based  evolutionary  algorithm  for  many-objective  optimization  problems.  IEEE  Trans.  on
                     Evolutionary Computation, 2019, 23(2): 173–187. [doi: 10.1109/TEVC.2018.2791283]
                 [25]   Dutta S, Raju MSS, Mallipeddi R, Das KN. Adaptive mating selection based on weighted indicator for multi/many-objective evolutionary
                     algorithm. Applied Soft Computing, 2023, 139: 110223. [doi: 10.1016/j.asoc.2023.110223]
                 [26]   Wang  HD,  Jiao  LC,  Yao  X.  Two_Arch2:  An  improved  two-archive  algorithm  for  many-objective  optimization.  IEEE  Trans.  on
                     Evolutionary Computation, 2015, 19(4): 524–541. [doi: 10.1109/TEVC.2014.2350987]
                 [27]   Ye TY, Wang H, Zeng T, Omran MGH, Wang F, Cui ZH, Zhao J. An improved two-archive artificial bee colony algorithm for many-
                     objective optimization. Expert Systems with Applications, 2024, 236: 121281. [doi: 10.1016/j.eswa.2023.121281]
                 [28]   Dai  C.  Two-archive  evolutionary  algorithm  based  on  multi-search  strategy  for  many-objective  optimization.  IEEE  Access,  2019,  7:
                     79277–79286. [doi: 10.1109/ACCESS.2019.2917899]
                 [29]   Cai L, Qu SR, Cheng GJ. Two-archive method for aggregation-based many-objective optimization. Information Sciences, 2018, 422:
                     305–317. [doi: 10.1016/j.ins.2017.08.078]
                 [30]   Liu ZQ, Sun N, Wu YM, Yang T, Liang X, Fang YC. Multi-objective trajectory planning for 5-DOF underactuated tower cranes with
                     state constraints. SCIENTIA SINICA Informationis, 2022, 52(3): 521–538 (in Chinese with English abstract). [doi: 10.1360/SSI-2021-
                     0106]
                 [31]   Li  W,  Gong  WY,  Ming  F,  Wang  L.  Constrained  multi-objective  evolutionary  algorithm  with  an  improved  two-archive  strategy.
                     Knowledge-based Systems, 2022, 246: 108732. [doi: 10.1016/j.knosys.2022.108732]
                 [32]   Xia MM, Dong MG. A novel two-archive evolutionary algorithm for constrained multi-objective optimization with small feasible regions.
                     Knowledge-based Systems, 2022, 237: 107693. [doi: 10.1016/j.knosys.2021.107693]
                 [33]   Liu YP, Yen GG, Gong DW. A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. IEEE
                     Trans. on Evolutionary Computation, 2019, 23(4): 660–674. [doi: 10.1109/TEVC.2018.2879406]
                 [34]   Li ZP, Zou J, Yang SX, Zheng JH. A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective
                     optimization. Information Sciences, 2021, 574: 413–430. [doi: 10.1016/j.ins.2021.05.075]
                 [35]   Das I, Dennis JE. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization
                     problems. SIAM Journal on Optimization, 1998, 8(3): 631–657. [doi: 10.1137/S1052623496307510]
                 [36]   Zitzler  E,  Laumanns  M,  Thiele  L.  SPEA2:  Improving  the  strength  Pareto  evolutionary  algorithm  for  multiobjective  optimization.  In:
                     Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Athens: Int’l Center for Numerical
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