A Novel Genetic Algorithm for Constrained Multimodal Multi-Objective Optimization Problems

Da Feng and Jianchang Liu ()
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Da Feng: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Jianchang Liu: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2025, vol. 13, issue 11, 1-33

Abstract: This paper proposes a multitasking-based genetic algorithm (MTGA-CMMO) to solve constrained multimodal multi-objective optimization problems (CMMOPs). In MTGA-CMMO, the main task is assisted by two auxiliary tasks to obtain all the feasible Pareto solution sets. The constraint boundaries of auxiliary task 1 are dynamically adjusted, facilitating the main task’s population in crossing infeasible regions early in the evolution and providing more evolutionary direction later in the evolution. Auxiliary task 2 can contribute to the exploitation ability of the main task. Meanwhile, a probability-based leader mating selection mechanism is devised to improve the global search capability of MTGA-CMMO. Additionally, three environmental selection strategies are designed to correspond to the different tasks in MTGA-CMMO. Extensive experimental verification demonstrates that MTGA-CMMO outperforms other comparative algorithms across multiple test instances and one practical application problem.

Keywords: constrained multimodal multi-objective optimization problem; evolutionary multitasking; mating selection; environmental selection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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