Affiliations: REGIM: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), Sfax, Tunisia | MIR Labs: Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, WA, USA | IT4Innovations - Center of excellence, VSB - Technical University of Ostrava, Czech Republic
Note: [] Corresponding author. Walid Elloumi, REGIM: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax 3038, Tunisia. E-mail: walid.elloumi@ieee.org
Abstract: In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOPSO-FACO). This hybridization solves the multi-objective problem, which relies on both time performance criteria and the shortest path. Experimental results illustrate that the proposed method is efficient.
