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This paper presents a new optimization algorithm: MP-CPSO, cooperative particle swarm optimizer based on multi-population. MP-CPSO is based on a master-slave model, slave swarms are employed to search best solution in the solution space independently and population size is adjusted adaptively based on multi-population Lotka-Volterra competition equation. The master swarm evolves based on its own knowledge and also the knowledge of the slave swarms. A disturbance factor is added to a particle swarm optimizer (PSO) for improving PSO algorithmspsila performance. When the time of the current global best solution having not been updated is longer than the disturbance factor, the particlespsila velocities will be reset in order to force swarms getting out of local minimum. The experiments of Flow-Shop Scheduling Problem (FSSP) optimizations are presented using MP-CPSO. The results validate the efficiency of the new algorithm presented in this paper.