Fuzzy Logic Control System-Assisted Operator Selection for Constrained Multiobjective Optimization

Constrained multi-objective evolutionary algorithms (CMOEAs) typically integrate diverse evolutionary operators, constraint-handling techniques, and environmental selection (ES) strategies to address constrained multi-objective optimization problems (CMOPs). Notably, significant performance variations emerge when identical CMOEAs employ different operators for solving the same CMOP, a phenomenon arising from distinct operator preferences exhibited by CMOPs with varied landscape. Therefore, it is worthwhile and promising to adaptively select appropriate operators for different CMOPs. This paper proposes CMOFLCS, a novel framework incorporating a fuzzy logic control system (FLCS). We introduce dual metrics for assessing population convergence and diversity, combined with a reward mechanism that dynamically evaluates operators' historical contributions. These metrics feed into the FLCS, which synergizes expert-defined rules with real-time data feedback to probabilistically select optimal operators via roulette wheel. Furthermore, we develop an angle-constrained ES that redirects inefficient exploration of the unconstrained Pareto front (UPF) to a uniform search of the objective space in the UPFto- constrained Pareto front (CPF) direction. This mechanism activates adaptively when handling problems with complete UPF-CPF separation. Experiments on 33 benchmark problems and 25 real-world applications demonstrate that CMOFLCS achieves superior performance, outperforming eight state-of-theart CMOEAs. Significantly, FLCS integration enhances baseline performance when embedded into two popular CMOEAs, further validating both its effectiveness and generalizability.