Particle swarm optimization with pbest crossover
Stephen Y. Chen · 2012
Particle swarm optimization can be viewed as a system with two populations: a population of current positions and a population of personal best attractors. In genetic algorithms, crossover is applied after selection - the goal is to create a new offspring solution using components from the best available solutions. In a particle swarm, the best available solutions are in the population of personal best attractors. Compared to standard particle swarm optimization, a modified version which periodically creates particle positions by crossing the personal best positions can achieve large improvements. These improvements are most consistent on multi-modal search spaces where the new crossover solutions may help the search process escape from local optima.