Adaptive Two-stage Large-scale Constrained Multi-objective Evolutionary Algorithm

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Adaptive Two-stage Large-scale Constrained Multi-objective Evolutionary Algorithm

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[1]YU Kunjie,YANG Zhenyu,QIAO Kangjia,et al.Adaptive Two-stage Large-scale Constrained Multi-objective Evolutionary Algorithm[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):1-9.[doi:10.13705/j.issn.1671-6833.2023.05.006]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 05 Page number: 1-9 Column: Public date: 2023-08-20

Title:: Adaptive Two-stage Large-scale Constrained Multi-objective Evolutionary Algorithm

Author(s):: YU Kunjie; YANG Zhenyu; QIAO Kangjia; LIANG Jing; YUE Caitong

Keywords:: large-scale constrained multi-objective optimization; algorithm; self-adaption; archive set; Pareto front; convergence rate; test function

CLC:: TP301

DOI:: 10.13705/j.issn.1671-6833.2023.05.006

Abstract:: To address the difficulties of slow convergence and difficulty in finding feasible solutions when solving large-scale constrained multi-objective optimization problems, an adaptive two-stage large-scale constrained multiobjective evolutionary algorithm was proposed. In the first stage, the algorithm adaptively selected some variables for optimization according to the nature of the decision variables, without considering any constraint to make the population quickly cross the infeasible region and approach the unconstrained Pareto front. In the second stage, the algorithm considered all the constraints and optimizes the variables as a whole using the ε constraint-handling technique. At the same time, the feasible and non-dominated solutions obtained in the evolutionary process were saved and updated using archive to continuously improve the convergence and diversity of the population. Finally, the proposed algorithm was experimentally compared with the other six algorithms on 37 test functions, and the results showed that the proposed algorithm could achieved the best results on 25 functions and outperforms the comparison algorithm on at least 31 functions, respectively; meanwhile, the feasibility rate of the proposed algorithm in more than 90% of the functions could reach 100%, which could effectively solve large-scale constrained multi-objective optimization problems.

References:: [1] 李二超, 李进. 两阶段三存档集约束优化算法(TSDA) [J]. 郑州大学学报(工学版), 2018, 39(6): 23-29.
LI E C, LI J. Constraint optimization algorithm with twostage and three-archive [ J] . Journal of Zhengzhou University (Engineering Science) , 2018, 39(6) : 23-29.
[2] 华一村, 刘奇奇, 郝矿荣, 等. 非规则 Pareto 前沿面多目标进化优化算法研究综述 [ J] . 郑州大学学报 (工学版) , 2021, 42(1) : 1-8.
HUA Y C, LIU Q Q, HAO K R, et al. A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(1): 1-8.
[3] 史非凡, 史旭华. 基于参考向量的自适应约束多目标进化算法[J]. 计算机应用, 2022, 42(2): 542-549.
SHI F F, SHI X H. Adaptive reference vector based constrained multi-objective evolutionary algorithm[J]. Journal of Computer Applications, 2022, 42(2): 542-549.
[4] 梁静, 刘睿, 瞿博阳, 等. 进化算法在大规模优化问题中的应用综述[ J] . 郑州大学学报(工学版) , 2018, 39(3) : 15-21.
LIANG J, LIU R, QU B Y, et al. A survey of evolutionary algorithms for large scale optimization problem [ J] . Journal of Zhengzhou University ( Engineering Science) , 2018, 39(3) : 15-21.
[5] EVERSON R M, FIELDSEND J E. Multiobjective optimization of safety related systems: an application to shortterm conflict alert[ J] . IEEE Transactions on Evolutionary Computation, 2006, 10(2) : 187-198.
[6] HE C, CHENG R, ZHANG C J, et al. Evolutionary large-scale multiobjective optimization for ratio error estimation of voltage transformers[ J] . IEEE Transactions on Evolutionary Computation, 2020, 24(5) : 868-881.
[7] TIAN Y, ZHANG X Y, WANG C, et al. An evolutionary algorithm for large-scale sparse multiobjective optimization problems[ J] . IEEE Transactions on Evolutionary Computation, 2020, 24(2) : 380-393.
[8] HE C, CHENG R, TIAN Y, et al. Paired offspring generation for constrained large-scale multiobjective optimization [ J ] . IEEE Transactions on Evolutionary Computation, 2021, 25(3) : 448-462.
[9] ZHANG X, MA Z B, DING B W, et al. A coevolutionary algorithm based on the auxiliary population for cstrained large-scale multi-objective supply chain network [ J] . Mathematical Biosciences and Engineering, 2022, 19(1) : 271-286.
[10] WANG B C, SHUI Z Y, FENG Y, et al. Evolutionary algorithm with dynamic population size for constrained multiobjective optimization [ J] . Swarm and Evolutionary Computation, 2022, 73: 101104.
[11] FAN Z, LI W J, CAI X Y, et al. Push and pull search for solving constrained multi-objective optimization problems[ J] . Swarm and Evolutionary Computation, 2019, 44: 665-679.
[12] MA H P, WEI H Y, TIAN Y, et al. A multi-stage evolutionary algorithm for multi-objective optimization with complex constraints [ J ] . Information Sciences, 2021, 560: 68-91.
[13] TIAN Y, ZHANG T, XIAO J H, et al. A coevolutionary framework for constrained multiobjective optimization problems[ J] . IEEE Transactions on Evolutionary Computation, 2021, 25(1) : 102-116.
[14] WANG J H, LIANG G X, ZHANG J. Cooperative differential evolution framework for constrained multiobjective optimization [ J ] . IEEE Transactions on Cybernetics, 2019, 49(6) : 2060-2072.
[15] GUPTA A, ONG Y S, FENG L. Multifactorial evolution: toward evolutionary multitasking [ J] . IEEE Transactions on Evolutionary Computation, 2016, 20(3) : 343-357.
[16] CHEN K, XUE B, ZHANG M J, et al. An evolutionary multitasking-based feature selection method for high-dimensional classification [ J ] . IEEE Transactions on Cybernetics, 2022, 52(7) : 7172-7186.
[17] QIAO K J, YU K J, QU B Y, et al. An evolutionary multitasking optimization framework for constrained multiobjective optimization problems[ J] . IEEE Transactions on Evolutionary Computation, 2022, 26(2) : 263-277.
[18] MA X L, LIU F, QI Y T, et al. A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables[ J] . IEEE Transactions on Evolutionary Computation, 2016, 20(2) : 275-298.
[19] ZHANG X Y, TIAN Y, CHENG R, et al. A decision variable clustering-based evolutionary algorithm for largescale many-objective optimization[ J] . IEEE Transactions on Evolutionary Computation, 2018, 22(1) : 97-112.
[20] FAN Z, LI W J, CAI X Y, et al. Difficulty adjustable and scalable constrained multiobjective test problem toolkit[ J] . Evolutionary Computation, 2020, 28 ( 3) : 339 -378.
[21] ZITZLER E, LAUMANNS M, THIELE L. SPEA2: improving the strength Pareto evolutionary algorithm [ EB OL] . (2001-05 - 10) [ 2022 - 06 - 13] . https:∥doi. org / 10. 3929 / ethz-a-004284029.
[22] WANG B C, LI H X, LI J P, et al. Composite differential evolution for constrained evolutionary optimization[ J] . IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(7) : 1482-1495.
[23] TIAN Y, ZHENG X T, ZHANG X Y, et al. Efficient large-scale multiobjective optimization based on a competitive swarm optimizer [ J] . IEEE Transactions on Cybernetics, 2020, 50(8) : 3696-3708.
[24] FAN Z, LI W J, CAI X Y, et al. An improved epsilon constraint-handling method in MOEA / D for CMOPs with large infeasible regions [ J] . Soft Computing, 2019, 23 (23) : 12491-12510.
[25] MA Z W, WANG Y. Evolutionary constrained multiobjective optimization: test suite construction and performance comparisons[ J] . IEEE Transactions on Evolutionary Computation, 2019, 23(6) : 972-986.
[26] TIAN Y, CHENG R, ZHANG X Y, et al. PlatEMO: a MATLAB platform for evolutionary multi-objective optimization educational forum[ J] . IEEE Computational Intelligence Magazine, 2017, 12(4) : 73-87.
[27] YU K J, LIANG J, QU B Y, et al. Dynamic selection preference-assisted constrained multiobjective differential evolution[ J] . IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(5) : 2954-2965.

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Last Update: 2023-09-03