基于动态混沌扰动的粒子群优化及其应用

针对混沌粒子群算法中存在的盲目搜索问题，提出基于动态混沌扰动的粒子群优化算法。对标准粒子群优化引入动态混沌扰动，在最优值改变时进行较小扰动，在多次不变时进行动态扰动范围的混沌扰动，减少混沌粒子群算法中存在的盲目搜索，提高搜索速度和效率，使有限的时间用在最有效的搜索上。将该算法应用到K均值算法中，可以克服K均值算法的局部最优和对初值和孤立点敏感的缺点，使K均值算法得到全局最优解。通过仿真实验证实该算法的高效性和稳定性。

Particle Swarm Optimization Based on Dynamic Chaotic Perturbations and Its Application

Aiming at the blind search of the chaotic particle swarm algorithm,Particle Swarm Optimization（PSO） based on dynamic chaotic perturbations is proposed.The dynamic chaotic perturbations are introduced for the standard PSO.Small disturbances are used when the optimal value changes.The chaotic disturbances within dynamical range of disturbances are used when the optimal value unchanges many times.It not only can reduce the blind search of the chaotic particle swarm algorithm,but also can improve the search speed and search efficiency,so that the limited time is spent on the most effective search.The algorithm is applied to the K-means algorithm,which can overcome the shortcomings of the local optimum and the sensitive to initial value in the K-means algorithm,and it can stably acquire the global optimal solution.The efficiency and stability of the algorithms are confirmed by the simulation experiments.