Parallel strength Pareto multi-objective evolutionary algorithm for optimization problems

Finding a good convergence and distribution of solutions near the Pareto-optimal front in a small computational time is an important issue in multi-objective evolutionary optimization. Previous studies have either demonstrated a good distribution with a large computational overhead or a not-so-good distribution quickly, Strength Pareto Evolutionary Algorithm (SPEA) produces a better distribution with larger computational effort. In this paper a Parallel Strength Pareto Multi-objective Evolutionary Algorithm (PSPMEA) is proposed. PSPMEA is a parallel computing model designed for solving Pareto-based multi-objective optimization problems by using an evolutionary procedure. In this procedure, both global parallelization and island parallel evolutionary algorithm models are implemented based on Java multi-threaded and distributed computation programtic technology separately. Each sub-population evolves separately with different crossover and mutation probability, but they exchange individuals in the elitist archive. The benchmark problems numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.