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Adaptive particle swarm optimization with mutation

机译：具有突变的自适应粒子群优化

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When an individual is closed to the optimal particle, its velocity will approximate to zero. This is the main reason why particle swarm optimization(PSO) algorithm is prone to trap into local minima. A new improved particle swarm optimization(IPSO) is proposed, in which is guaranteed to converge to the global optimization solution with probability one. During the running time, the mutation probability for the current particle is determined by the variance of the individual's concentration and convergence function. The ability of IPSO to break away from the local optimum is greatly improved by the mutation. The concept of adaptive acceleration factor is introduced to the IPSO. In this manner, the global and local search capability can be coordinated to make for locating the global optimum quickly. Finally, IPSO is applied to optimize several test functions. Test results show that IPSO can find global optima effectively.
机译：当个体关闭到最佳粒子时，其速度将近似为零。这是粒子群优化（PSO）算法容易陷入局部最小值的主要原因。提出了一种新的改进粒子群优化（IPSO），其中保证将与概率1的全局优化解决方案收敛到。在运行时间期间，通过个体浓度和收敛函数的方差来确定电流粒子的突变概率。突变极大地改善了IPSO突破局部最佳的能力。将自适应加速因子的概念引入IPSO。以这种方式，可以协调全局和本地搜索能力，以便快速定位全局最佳。最后，应用IPSO以优化多个测试功能。测试结果表明，IPSO可以有效地找到全球Optima。

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