Li Desheng
Anhui Science and Technology University, Fengyang, Anhui 233100, China.
ScientificWorldJournal. 2014 Mar 3;2014:370691. doi: 10.1155/2014/370691. eCollection 2014.
This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles' activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem.
本文提出了一种新型的合作量子行为粒子群优化（CQPSO）算法的变体，即CQPSO-DVSA-LFD，它具有两种机制来减少搜索空间并避免停滞。一种机制称为动态可变搜索区域（DVSA），负责将粒子的活动范围限制在一个缩小的区域内。另一方面，为了逃离局部最优，采用莱维飞行在粒子运动中产生随机扰动。为了测试CQPSO-DVSA-LFD的性能，进行了数值实验，将该算法与不同变体的粒子群优化算法进行比较。根据实验结果，所提出的方法在基准测试函数和组合优化问题（即作业车间调度问题）上均比粒子群优化算法的其他变体表现更好。
