Experience repository based Particle Swarm Optimization and its application to biped robot walking

摘要

In this paper, experience repository based Particle Swarm Optimization (ERPSO) is suggested for effectively applying Particle Swarm Optimization (PSO) to real life problems. The ERPSO uses a concept experience repository to store previous position and fitness of particles to accelerate convergence speed of PSO. The proposed method was compared with PSO variants in a three dimensional dynamic simulator for the bipedal walking. The ERPSO found the best fitness value and Central Pattern Generator parameters that could produce a walking of a biped robot. And ERPSO has fast convergence property which reduces the evaluation of fitness of parameters in a real environment.