【摘要】 针对当前车间调度多目标优化研究存在收敛速度慢、精度低的问题,提出了混沌多目标粒子群优化算法。在算法中,设计了一种新的叠加Logistic扰动的Tent混沌映射算子,通过该算子周期性地更新种群以保证种群的多样性;对收缩粒子群算法进行了扩展使其能够快速收敛到Pareto前沿。通过标准测试问题与实际应用对所提方法进行了验证,实验结果显示混沌多目标粒子群优化算法无论在收敛速度还是在优化精度上都优于其它典型多目标进化算法。更多还原

【Abstract】 Since the current job shop scheduling multi- objective optimization has the drawbacks of slow convergence speed and low accuracy,it proposes a chaotic multi- objective particle swarm optimization algorithm. In the algorithm,designed the Tent chaotic mapping a new stack Logistic disturbance,the operator periodically update population in order to ensure the diversity of population; on the contraction of particle swarm algorithm is extended so that it can rapidly converge to the Pareto front. The standard test problems and practical application to verify the proposed method,experimental results show that the chaotic multi- objective particle swarm optimization algorithm both in convergence speed and optimization accuracy is better than other typical multi- objective evolutionary algorithm.更多还原