摘要
提出了一种结合禁忌搜索策略的邻居结构粒子群优化算法。将粒子组成邻居结构,为了避免算法陷入局部最优,在粒子群算法中,引入了禁忌搜索策略,对当前适应值差的粒子进行替换,获得全局最优值。通过2个标准测试函数优化,与其他优化算法比较。可以看出,该算法能够明显提升粒子群算法的寻优性能。
This study proposes a particle swarm optimization( PSO) algorithm based on the neighborhood structure of Tabu search strategy. In order to avoid the algorithm falling into local optimum,Tabu search strategy is introduced in particle swarm optimization,which replaces the particles with poor fitness and obtains the global optimum. Two standard test functions are optimized and compared with other optimization algorithms. It can be seen that the algorithm can significantly improve the performance of particle swarm optimization.
引文
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