Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.
This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
Elhossini
Ahmed
A
School of Engineering, University of Guelph, Guelph, ON, Canada. aelhossi@uoguelph.ca
Areibi
Shawki
S
Dony
Robert
R
D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D057386:Search Engine / Q000379:methods*