A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator

Kouka, Najwa; BenSaid, Fatma; Fdhila, Raja; Fourati, Rahma; Hussain, Amir; Alimi, Adel M.

Abstract

Most evolutionary algorithms, including particle swarm optimization (PSO), use Pareto dominance as a major selection criterion and face significant challenges when dealing with many-objective problems. To tackle this issue, this paper proposes a novel algorithm, termed: Many- Objective PSO with Cooperative Agents (MaOPSO-CA). This exploits an Inverted Generational Distance (IGD) indicator in two innovative ways: firstly, as a leader selection method to select the preferable solution in terms of convergence and diversity, and, secondly, as an archiving method to decide which non-dominated solutions are kept in a bounded archive. The proposed strategy significantly promotes selection pressure toward the Pareto front. The results indicate that the IGD-based selection circumvents the issue of a large ratio of non-dominated solutions that exist in MaOPs. Moreover, a multi-swarm is investigated and modeled as a Multi-Agent System (MAS), so that knowledge sharing among different sub-swarms is easily improved through automated negotiation. The effectiveness of our proposed algorithm is validated with numerous experimental studies in solving 110 benchmark testing instances with up to twenty objectives. Experimental results demonstrate the effectiveness of the new algorithm compared to recent state-of-the-art methods. Finally, the application of MaOPSO-CA to a challenging, real-world water resource-management problem is shown to produce very encouraging results, demonstrating its potential as a benchmark resource for the research community.

Citation

Kouka, N., BenSaid, F., Fdhila, R., Fourati, R., Hussain, A., & Alimi, A. M. (2023). A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator. Information Sciences, 623, 220-241. https://doi.org/10.1016/j.ins.2022.12.021

• Journal Article Type: Article
• Acceptance Date: Dec 7, 2022
• Online Publication Date: Dec 13, 2022
• Publication Date: 2023-04
• Deposit Date: Jan 27, 2023
• Publicly Available Date: Dec 14, 2023
• Journal: Information Sciences
• Print ISSN: 0020-0255
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 623
• Pages: 220-241
• DOI: https://doi.org/10.1016/j.ins.2022.12.021