基于随机评价机制的交互式双子群QPSO算法

通过两组势阱中心不同且相互协同的主、辅子群，在具有量子行为的粒子群优化(QPSO)算法基础上构造一种基于随机评价机制的交互式双子群QPSO算法(DIR-QPSO)。该算法通过子群间的协作避免了种群多样性的快速消失，增强了算法的全局搜索能力。同时，随机因子的加入进一步提高了粒子摆脱局部极值的能力。对6个测试函数的实验结果表明， DIR-QPSO算法相对于传统的粒子群优化算法(PSO)在处理单峰和多峰函数时具有更好的优化性能，收敛速度和收敛精度都得到了较大的提高。

Improved dual-group interaction QPSO algorithm based on random evaluation

The dual-group interaction quantum-behaved particle swarm optimization(QPSO) algorithm based on random evaluation(DIR-QPSO) is proposed by constructing the master-slave sub-groups with different potential well centers, which avoids the rapid disappearance of swarm diversity and enhances the global searching ability through collaboration between sub-groups. Meanwhile, the involvement of random factor further improves the particles’ ability to escape from local extremums. Experiment results on 6 testing functions show that the DIR-QPSO algorithm outperforms the traditional particle swarm optimization(PSO) algorithm regarding the optimization of unimodal and multimodal functions, with enhancement in both convergence speed and precision.