置换流水车间调度的文化量子粒子群算法

Permutation flow shop scheduling algorithm based on cultural-based quantum particle swarm optimization

针对量子粒子群算法在求解置换流水车间调度问题时容易早熟,提出用文化量子粒子群算法求解置换流水车间调度问题。该算法的主群体空间采用量子粒子群算法,知识空间采用遗传算法。通过影响操作和接受操作,知识空间定期将自己的精英个体贡献给主群体空间,主群体空间也定期将自己的精英个体贡献给知识空间。最后将该算法应用到具体问题的求解,用MATLAB编程仿真测试,仿真结果表明,该算法收敛速度快,且具有较高的求解质量,而且其搜索性能优于一般的量子粒子群算法。

Coping with such disadvantages of quantum particle swarm optimization algorithm being easy to run into local optima,the method that Cultural-based quantum particle swarm optimization is proposed to be applied to permutation flow shop scheduling algorithm. This algorithm model consists of a QPSO-based main population space and a GA-based knowledge space, The main population space contributes elite individuals to the knowledge space periodically,and knowledge space also contributes elite individuals to the main population space .then this methodology is applied to permutation flow shop scheduling problem,the algorithm is tested with MATLAB simulation,and the result shows that this algorithm has better answers and more rapid convergence and much better than quantum particle swarm optimizations.