Title: A constrained multi-objective optimisation with weak-side complementary and dynamically guided

Authors: Ziqiong Liu; Sanfeng Chen; Xiang Du; Wei Li; Hui Wang

Addresses: School of Software, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, 518000, China ' School of Software, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, 518000, China ' School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, 341000, China ' School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, 341000, China ' School of Software, Shenzhen Institute of Information Technology, Shenzhen, Guangdong, 518000, China

Abstract: At the current stage, many complex optimisation problems can be transformed into constrained multi-objective optimisation problems (CMOPs). Constrained multi-objective evolutionary algorithms (CMOEAs) have become an efficient way of resolving constrained multi-objective problems. However, CMOPs will face huge challenges such as complexity of constraints, difficulty in exploring, and serious conflicts between objective functions and constraints. This paper proposes an efficient weak-side complementary and dynamically guided CMOEA named WDCMO. WDCMO has two populations: the main population and the auxiliary population. Its evolutionary process is divided into two stages: in the first stage, the WDCMO main and auxiliary populations focus on the exploration of two different regions; in the second stage, the WDCMO auxiliary population uses the information about the value of the objective function to guide the main population's evolution. Finally, WDCMO was tested against three other algorithms on a test suite. The experimental results show that the test values of WDCMO are clearly better than the other comparison algorithms on the majority of test problems. Specifically, WDCMO achieved 9 HV indicator leads and 12 IGD indicator leads on 14 constrained multi-objective problems.

Keywords: constrained multi-objective; co-evolution; evolutionary algorithms.

DOI: 10.1504/IJCSM.2025.147462

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.3, pp.219 - 230

Received: 03 Sep 2024
Accepted: 25 Jan 2025
Published online: 16 Jul 2025 *

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