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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.