Job Shop Scheduling Based on an Improved Cooperative Particle Swarm Optimization

Aiming at the stagnation existing in the cooperative particle swarm optimization, this paper presents an improved cooperative particle swarm optimization algorithm. The algorithm uses an optimized sub-swarms cooperation mode with a disturbance mechanism to ensure the convergence rate. Meanwhile, a comprehensive learning strategy is introduced to strengthen the diversity of population to prevent the stagnation. The new algorithm is applied to job shop scheduling problems. The results of simulation experiments show that the new algorithm conquers the stagnation effectively, improves the global convergence ability, and has better optimization performance than basic cooperative particle swarm optimization.