Dynamic Population-Based Particle Swarm Optimization Combined with Crossover Operator

摘要

Particle swarm optimization (PSO) is a new swarm intelligent optimization technique. Although it maintains a fast convergent speed, it is still easy trapped into a local optimum when dealing with high-dimensional numerical problems. To overcome this shortcoming, in this paper, a new variant of PSO is designed hybrid with a dynamic population strategy and crossover operator. Simulation results show this new variant is superior to two other previous modifications in high-dimensional multi-model benchmarks.