Optimising objectives and satisfying constraints present significant challenges in solving constrained multi-objective optimisation problems. In this paper, we propose an algorithm that incorporates the push-and-pull ...
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Optimising objectives and satisfying constraints present significant challenges in solving constrained multi-objective optimisation problems. In this paper, we propose an algorithm that incorporates the push-and-pull search framework and a two-ranking fitness function named ToR-PPS. The algorithm is divided into three stages: the push stage, transitional stage, and pull stage. In the push stage, the population is directed toward the unconstrained Pareto front, without consideration of constraints. In the transitional stage, a diversity expansion strategy is proposed to optimise the diversity of the population. In the pull stage, the fitness function with two rankings is utilised to pull the population toward the constrained Pareto front. Experiments are conducted to compare the algorithm with five state-of-the-art constrainedmulti-objectiveoptimisation evolutionary algorithms on two benchmark suites. The results clearly illustrate the superiority and efficiency of the algorithm.
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