Title: Fuzzy set induced co-evolutionary approach to many objective optimisation

Authors: Selina Khoirom; Pratyusha Rakshit

Addresses: Department of Electronics and Telecommunication Engineering, Jadavpur University, India ' Department of Electronics and Telecommunication Engineering, Jadavpur University, India

Abstract: The research introduces a new many-objective optimisation (MaOO) technique that utilises the parallel nature of evolutionary processes through co-evolution. This method applies the artificial bee colony (ABC) algorithm to tackle individual objectives simultaneously resembling co-evolution. Once convergence is achieved, a selection of top-quality solutions is carefully chosen from each ABC population, and each concentrating on a specific objective. These selections are then combined into a single set. Following this, a ranking system based on fuzzy membership is created to identify the best-performing elements within this combined set, representing the approximate Pareto optimal solutions for the given MaOO problem. The proposed algorithm, called co-evolutionary fuzzy-bee colony (CFBC), is tested against three advanced techniques. The experimental outcomes show that CFBC outperforms its competitors in terms of performance metrics. This innovative MaOO approach provides a promising method for optimising complex problems with multiple objectives, potentially improving decision-making processes in various fields of application.

Keywords: artificial bee colony; ABC; many-objective optimisation; MaOO; parallel optimisation; fuzzy membership; multiple population.

DOI: 10.1504/IJICA.2025.149781

International Journal of Innovative Computing and Applications, 2025 Vol.15 No.4, pp.207 - 219

Received: 28 Mar 2025
Accepted: 07 Jul 2025
Published online: 12 Nov 2025 *

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