A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multitasking

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A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multitasking

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[1]ZHANG Meng,JING Liang,QIAO Kangjia,et al.A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multitasking[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-9.[doi:10.13705/j.issn.1671-6833.2025.05.021]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-9 Column: Public date: 2026-09-10

Title:: A Constrained Multi-objective Evolutionary Algorithm Based on Competition and Cooperation Multitasking

Author(s):: ZHANG Meng¹ ; JING Liang² ; QIAO Kangjia² ; YUE Caitong² ; WANG Xilu³; 1. College of Energy and Intelligent Engineering, Henan College of Animal Husbandry and Economics, Zheng zhou 450046, China; 2. School of Electrical and Information Engineering, Zhengzhou Universit y, Zhengzhou 450001, China;3. School of Computer Science and Electronic Engineering, University of Surrey, Surrey GU2 7XH, U. K.

Keywords:: Constrained multi-objective optimization; Evolutionary algorithm; Multitasking; Resource allocation; Collaborative optimization

CLC:: TP301

DOI:: 10.13705/j.issn.1671-6833.2025.05.021

Abstract:: Constrained multi-objective evolutionary algorithm based on multitasking has shortcomings in resource allocation and collaborative optimization, resulting in low effectiveness populations wasting computational resources and underutilized high-quality solution information. Therefore, this paper proposes a constrained multi-objective evolutionary algorithm based on competitive and cooperative multitasking, which includes two main strategies: firstly, a competition-based resource allocation strategy is proposed, which achieves adaptive allocation of computing resources based on the historical performance of each task population; Secondly, a collaborative optimization strategy based on parent aggregation and offspring diffusion is designed, which generates high-quality offspring through cross-task cooperation and spreads them to various task populations, achieving efficient utilization of effective information. The proposed algorithm is compared with five other advanced algorithms (CMOEA_MS, cDPEA, EMCMO, MTCMO, and CMOEMT) on 38 test functions, and the results show that the proposed algorithm achieves optimal results on 25 and 26 functions under IGD and HV indicators, respectively, and is superior to the compared algorithms on at least 23 and 24 functions, respectively; The proposed algorithm has a feasibility rate of 100% on all functions and can effectively solve constrained multi-objective optimization problems.

References:: [1] 李二超, 李进. 两阶段三存档集约约束优化算法(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 two-stage and three-archive [J]. Journal of Zhengzhou University (Engineering Science), 2018, 39(6): 23-29.
[2] 陈少森, 陈瑞, 梁伟, 等. 面向复杂约束优化问题的进化算法综述[J]. 软件学报, 2023, 34(2): 565-581.
CHEN S M, CHEN R, LIANG W, et al. Overview of evolutionary algorithms for complex constrained optimization problems [J]. Journal of Software, 2023, 34(2): 565-581.
[3] 王林锋, 揭丽琳, 黎明, 等. 基于自适应双阶段分级均衡的约束高维多目标进化算法[J]. 控制与决策, 2024, 40(5): 1512-1522.
WANG L F, JIE L L, LI M, et al. Constrained many-objective evolutionary algorithm based on adaptive two-stage hierarchical equilibrium [J]. Control and Decision, 2024, 40(5): 1512-1522.
[4] FAN Z, LI W, CAI X, et al. Push and pull search for solving constrained multi-objective optimization problems [J]. Swarm and Evolutionary Computation, 2019, 44: 665-679.
[5] TIAN Y, ZHANG Y, SU Y, et al. Balancing objective optimization and constraint satisfaction in constrained evolutionary multiobjective optimization [J]. IEEE Transactions on Cybernetics, 2021, 52(9): 9559-9572.
[6] TIAN Y, ZHANG T, XIAO J, et al. A coevolutionary framework for constrained multiobjective optimization problems [J]. IEEE Transactions on Evolutionary Computation, 2020, 25(1): 102-116.
[7] MING M, TRIVEDI A, WANG R, et al. A dual-population-based evolutionary algorithm for constrained multiobjective optimization [J]. IEEE Transactions on Evolutionary Computation, 2021, 25(4): 739-753.
[8] 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.
[9] QIAO K J, YU K J, QU B Y, et al. Dynamic auxiliary task-based evolutionary multitasking for constrained multiobjective optimization [J]. IEEE Transactions on Evolutionary Computation, 2022, 27(3): 642-656.
[10] LI G, WANG Z, GAO W, et al. Decoupling constraint: task clone-based multi-tasking optimization for constrained multi-objective optimization [J]. IEEE Transactions on Evolutionary Computation, 2025, 29(2): 404-417.
[11] MING F, GONG W, WANG L, et al. Constrained multiobjective optimization via multitasking and knowledge transfer [J]. IEEE Transactions on Evolutionary Computation, 2022, 28(1): 77-89.
[12] YE Q, WANG W, LI G, et al. A self-organizing assisted multi-task algorithm for constrained multi-objective optimization problems [J]. Information Sciences, 2024, 664: 120339-120360.
[13] WANG J, LI Y, ZHANG Q, et al. Cooperative multiobjective evolutionary algorithm with propulsive population for constrained multiobjective optimization [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(6): 3476-3491.
[14] GARCÍA J L L, MONROY R, HERNÁNDEZ V A S, et al. COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems [J]. Swarm and Evolutionary Computation, 2021, 67: 100983-100997.
[15] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[16] WANG C, LIU Z, QIU J, et al. Adaptive constraint handling technique selection for constrained multi-objective optimization [J]. Swarm and Evolutionary Computation, 2024, 86: 101488-101503.
[17] SONG S, ZHANG K, ZHANG L, et al. A dual-population algorithm based on self-adaptive epsilon method for constrained multi-objective optimization [J]. Information Sciences, 2025, 655: 119096-119923.
[18] LIU Z Z, QIN Y, SONG W, et al. Multiobjective-based constraint-handling technique for evolutionary constrained multiobjective optimization: a new perspective [J]. IEEE Transactions on Evolutionary Computation, 2022, 27(5): 1370-1384.
[19] FAN Z, LI W, CAI X, et al. An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions [J]. Soft Computing, 2019, 23: 12491-12510.
[20] MING M, WANG R, ISHIBUCHI H, et al. A novel dual-stage dual-population evolutionary algorithm for constrained multiobjective optimization [J]. IEEE Transactions on Evolutionary Computation, 2021, 26(5): 1129-1143.
[21] FAN Z, LI W, CAI X, et al. Difficulty adjustable and scalable constrained multiobjective test problem toolkit [J]. Evolutionary Computation, 2020, 28(3): 339-378.
[22] QIAO K, LIANG J, YU K, et al. Evolutionary constrained multiobjective optimization: Scalable high-dimensional constraint benchmarks and algorithm [J]. IEEE Transactions on Evolutionary Computation, 2023, 28(4): 965-979.
[23] ZHONG X, YAO X, QIAO K, et al. A multitasking-based constrained multi-objective evolutionary algorithm with forward and backward stages [J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(5): 3474-3488.
[24] 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.

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Last Update: 2026-01-15