Parallel strength pareto multi-objective evolutionary algorithm for optimization problems
Shengwu Xiong, Feng Li · 2004
Finding a good convergence and distribution of solutions near the Pareto-optimal front in a small computational time is an important issue in multiobjective 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. A Parallel strength Pareto multiobjective evolutionary algorithm (PSPMEA) is proposed. PSPMEA is a parallel computing model designed for solving Pareto-based multiobjective 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 programmatic technology separately. Each subpopulation 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.