Multi-objective Optimization with Combination of Particle Swarm and Extremal Optimization for Constrained Engineering Design

Engineering optimization problems usually have several conflicting objectives, such that no single solution can be considered optimum with respect to all objectives. In recent years, many efforts have focused on hybrid metaheuristic approaches for their robustness and efficiency to solve the above-mentioned multiobjective optimization problems (MOPs). This paper proposes a novel hybrid algorithm with the integration of particle swarm optimization (PSO) and bio-inspired computational intelligence extremal optimization (EO) for constrained engineering design, which combines the superior functionalities of PSO for search efficency and extremal dynamics oriented EO for global search capability. The performance of proposed PSO-EO algorithm is further tested on several benchmark MOPs in comparison with reported results. The simulations show that the PSO-EO is effective in solving MOPs, could result in faster convergence and better spread. Key-Words: Multi-objective optimization, Evolutionary algorithm, Particle swarm optimization, Extremal optimization, Pareto dominance, Engineering design