Dynamic population strategy assisted Particle Swarm Optimization

In this paper, the authors propose two new evolutionary approaches to Multiobjective Optimization Problems (MOPs)-Dynamic Particle Swarm Optimization (DPSMO) and Dynamic Particle Swarm Evolutionary Algorithm (DPSEA). In DPSMO, instead of using genetic operators (e.g., crossover and mutation), the information sharing technique in Partide Swarm Optimization (PSO) is applied to inform the entire population more accurate moving direction and speed as opposed to any generic evolutionary algorithms (EA). Meanwhile, based on the dynamic population strategies, cell-based rank and density estimation and objective space compression strategy used in Dynamic Multiobjective Evolutionary Algorithm (DMOEA), the DPSMO can evolve to an approximately optimal population size while the population is approaching the true Pareto front. To overcome DPSMO's difficulty in producing a high-quality Pareto front, DPSEA is designed by combining both EA and PSO's information sharing techniques. By examining the selected performance measures on one test function, DPSEA is found to be competitive with, or even superior to DMOEA and DPSMO in terms of keeping the diversity of the individuals along the trade-off surface, tending to extend the Pareto front to new areas and finding a well-approximated Pareto optimal front.