Title: Constrained multi-objective optimisation with collaboration-driven strategy and infeasible knowledge-based cooperation

Authors: Bin Xu; Haifeng Zhang; Xiaodan Miao; Lili Tao

Addresses: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China ' School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China ' School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China ' School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, 201209, China

Abstract: Most existing constrained multi-objective evolutionary algorithms adopt unitary environmental selection and reproduction operator, which face the issue of striking a proper balance between objectives and constraints. To this end, we developed a new method with collaboration-driven strategies and infeasible knowledge-based cooperation. The main idea of this approach is using heterogeneous frameworks and operators. Specifically, the main population is maintained using the domination-based framework and constrained-domination principle. An auxiliary population is maintained using the decomposition-based framework and epsilon-constrained method. Meanwhile, the infeasible knowledge-based genetic algorithm and differential evolution are employed to generate offspring. The superior of this algorithm is validated by comparing with some popular methods with distinct characteristics on four suites. Experimental results indicated that our method performs best among all competitors on 62.5%, 50%, 50%, and 25.56% for CTPs, MWs, LIRCMOPs, and DOACMOPs, respectively. The effectiveness of collaboration-driven and infeasible knowledge-based cooperation are also verified by some ablation studies.

Keywords: constrained multi-objective optimisation; collaboration-driven; infeasible knowledge; cooperation; reproduction operator.

DOI: 10.1504/IJBIC.2025.146421

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.3, pp.188 - 204

Received: 18 Jun 2024
Accepted: 27 Feb 2025
Published online: 28 May 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article