【摘要】 为了保持全局搜索和局部搜索之间的均衡,在二进制QPSO算法中引入全面学习和合作方法,提出了一种新的二进制量子行为粒子群优化算法(CCBQPSO).完全学习策略可以保持群体的多样性,合作方法可以直接将算法引入到本地搜索,并快速收敛到最优解.在该算法中,所有粒子的个体最优位置可以首先参与到本地吸引子的更新,每个粒子的新解向量维度将依次取代对应粒子的先前个体最优位置和群体的全局最优位置的维度,并计算出适应值.最后使用5个测试函数对CCBQPSO的性能进行了测试,结果表明该算法可以增加群体的多样性,且提高了算法的收敛速度.更多还原

【Abstract】 To keep the balance between the global search and local search,a new binary quantum behavior particle swarm optimization algorithm(CCBQPSO)is proposed by introducing a comprehensive learning and cooperative method in the binary QPSO algorithm.The complete learning strategy can keep the diversity of the population,and the cooperative method can directly introduce the algorithm into the local search and converge to the optimal solution quickly.In this algorithm,the individual optimal position of all particles can first participate in the updating of the local attractor;and the new solution vector dimension of each particle will replace the previous optimal individual position of the corresponding particle and the global optimal position of the population and calculate the fitness value.The results show that the proposed algorithm can increase diversity of swarm and converge more rapidly than other binary algorithms.更多还原