A multi-stage evolutionary algorithm based on finite state machine theory for constrained multi-objective optimization problem

Multi-stage optimization algorithms are promising for solving constrained multi-objective optimization problems (CMOPs), particularly when dealing with complex feasible regions. Although decomposing the problem into multiple stages does simplify the optimization process, the timing of stage switchin...

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Vydané v:Swarm and evolutionary computation Ročník 100; s. 102222
Hlavní autori: Li, Zhuoxuan, Liu, Zhaoguang, Su, Changgeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.01.2026
Predmet:
Constrained multi-objective optimization
Evolutionary algorithm
Finite state machine
Multi-stage
Constrained multi-objective optimization
Finite state machine
Evolutionary algorithm
Multi-stage
ISSN:2210-6502
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Shrnutí:Multi-stage optimization algorithms are promising for solving constrained multi-objective optimization problems (CMOPs), particularly when dealing with complex feasible regions. Although decomposing the problem into multiple stages does simplify the optimization process, the timing of stage switching is crucial. However, current approaches tend to focus on update strategies while lacking adequate emphasis on timely stage switching. This study introduces a multi-stage evolutionary algorithm based on finite state machine theory, named FSM-CMO. The algorithm employs a finite set of states with predictable switching rules, utilizing event/action-driven feedback to trigger timely shifts between customized update strategies. FSM-CMO operates through four states. In State I, the main population (MPop) uses the weight vectors of the Helping population (HPop) through restricted mating selection for initial exploration. In State II, HPop disregards constraints to drive MPop toward the unconstrained Pareto front. In State III, HPop employs a new sparsity evaluation strategy to assist MPop in gradually exploring the feasible boundary of the current region. Finally, in State IV, HPop uses the truncation strategy in SPEA2 to adjust the distribution of MPop. Extensive tests on four benchmark suites and fourteen real-world CMOPs have demonstrated its competitive performance compared to seven state-of-the-art algorithms. Its key strength lies in the state switching mechanism that flexibly schedules strategies based on population status, enhancing adaptability to complex constraints. Although the framework excels in tested scenarios, future work will target ultra-high-dimensional and dynamically constrained problems to enhance its practical applicability.
ISSN:2210-6502
DOI:10.1016/j.swevo.2025.102222