基于禁忌搜索的动态粒子群算法

惯性权重线性递减的线性群粒子算法往往不能反映实际的优化搜索过程。动态粒子群算法虽然能较好地实现非线性的搜索,但是更容易陷入局部最优。提出了基于禁忌搜索的动态粒子群算法,引入了禁忌搜索的思想,来解决动态粒子群算法的容易陷入局部最优问题;并对禁忌公式进行了修改,使其不仅可以解决极小值最优问题,也可以解决极大值最优问题。根据实验结果,改进的算法不仅较好地避免了陷入局部最优,而且收敛速度也有提高。

Dynamic particle swarm algorithm based on Tabu search

Linear Particle Swarm Optimization algorithm which makes the inertia weight reduction linearly often fails to reflect the actual optimized search process.Dynamic particle swarm algorithm can be used to achieve the nonlinear search，but it is easy to fall into local optimization.Tabu search based dynamic particle swarm algorithm was presented.Tabu search was introduced to settle local optimization of dynamic particle swarm algorithm.And carried on a modification to Tabu Search’s formula，make it can solve the problems both the minimum optimal and the maximum optimal.According to experiment result，TS-DCWPSO algorithm not only avoids falling into local optimization but also improves the optimal speed.