Constraint handling procedure for multiobjective particle swarm optimization
Gary G. Yen, Wen Fung Leong · 2010
In this paper, the proposed constrained multiobejctive particle swarm optimization (MOPSO) adopts the multiobjective constraint handling framework and includes the following design features: An infeasible global best archive to guide the infeasible particles towards feasible region(s); procedures to update the personal best archive are designed to encourage finding feasible regions and convergence towards the Pareto front; acceleration constants in the particle swarm optimization equation are adjusted during the search process to encourage finding more feasible particles or to search for better solutions; and mutation operators are adopted to encourage global and local searches. The simulation results indicate that the proposed algorithm is highly competitive in solving the benchmark problems.