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中文摘要：
针对粒子群优化（PSO）算法搜索空间有限,容易陷入局部最优点的缺陷,提出一种新的量子粒子群优化算法——含维变异算子的量子粒子群算法（QPSODMO）。计算每一维的收敛度,以一定的概率对收敛度最小的维进行变异,让所有粒子在该维上的位置重新均匀分布在可行区域上。对测试函数所做的对比实验表明,所提出的QPSODMO增强了全局搜索能力,克服了PSO算法易于收敛到局部最优的缺点,也优于原始的量子粒子群算法。
英文摘要：
According to the limitation of particle swarm optimization（PSO） algorithm as finite sampling space,being easy to run into local optima,a new quantum-behaved particle swarm optimization with dimension mutation operator（QPSODMO） is presented.Based on this algorithm,the degrees of convergence of every dimension are calculated in every iteration from the beginning of mutation.The dimension of minimal convergent degree is mutated according to some probability,the positions of all particles in this dimension are dis-tributed in the range [-xmax,xmax] evenly.Comparative experiments on testing functions indicate that the QPSODMO enhances the global searching ability and the probability of successful searching,and overcomes the original PSO＇S liability to convergence to local optimum.It is also superior to traditional quantum-behaved particle swarm optimization（QPSO） algorithm.
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