自适应变异的粒子群优化算法

针对粒子群算法的早熟收敛问题，提出一种新的基于群体适应度变化率自适应变异的粒子群优化算法。该算法根据群体适应度变化率自适应调整惯性权重的取值，根据当前种群的平均粒距对种群中部分粒子进行变异操作。自适应调整与变异操作能增强算法跳出局部最优的能力，增大寻找全局最优的几率。对几种典型函数的测试结果表明，新算法的全局搜索能力有了明显的提高，有效避免了早熟收敛问题。

Particle Swarm Optimization Algorithm with Adaptive Mutation

Considering the premature convergence problem of Particle Swarm Optimization(PSO), a new Adaptive Particle Swarm Optimization with Mutation(APSOwM) is presented based on the variance ratio of population’s fitness. During the running time, the inertia weight and the mutation probability are determined by two factors: the variance ratio of population’s fitness and the average distance of current population. The ability of APSOwM to break away from the local optimum and to find the global optimum is greatly improved by the adaptive mutation. Experimental results show that the new algorithm is with great advantage of convergence property over PSO, and also avoids the premature convergence problem effectively.