Title: Fitness function with two rankings embedded in a push and pull search framework for constrained multi-objective optimisation problems

Authors: Kangshun Li; Jiaxin Xu; Shumin Xie; Hui Wang

Addresses: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China; School of Artificial Intelligence, Dongguan City University, Dongguan, 523109, China ' College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China ' College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China ' School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen, 518055, China

Abstract: 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 constrained multi-objective optimisation evolutionary algorithms on two benchmark suites. The results clearly illustrate the superiority and efficiency of the algorithm.

Keywords: constrained multi-objective optimisation problems; CMOPs; evolutionary algorithms; push and pull search framework; transitional stage; diversity.

DOI: 10.1504/IJBIC.2024.141673

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.3, pp.164 - 175

Received: 26 Sep 2023
Accepted: 14 Jan 2024
Published online: 30 Sep 2024 *

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