A boundary processing technique is presented to overcome the adverse effects of the convergence performance of particle swarm optimization caused by particles flying out of the search space. Firstly, the search boundaries for each dimension are interconnected to construct a closed search space with logically closed boundaries. Secondly, the formula for updating the position of particles is adjusted to control particles within the search space. The problem of particles flying out of the search space is a-voided by adjusting the updating techniques of differential vectors for both individual knowledge and society knowledge, which are located at the formula of updating the velocity of particles. As a result, particles are guided to search the global optimum solution more efficiently and steadily in the feasible solution space. The proposed method shows more robust and consistent optimization performance on the benchmark problems than the exiting boundary conditions regardless of the position of the global optimum solution.%针对粒子出界问题对微粒群优化算法收敛性能产生的不利影响,本文提出一种搜索空间边界连接的边界处理算法.该算法首先将搜索空间每一维的上下边界连接,形成一个逻辑上闭合的搜索空间,然后通过调整该空间中粒子位置的更新策略以及粒子速度更新公式中个体认知和社会认知差分向量的计算方法,消除了边界对飞行粒子的不利影响,使粒子在可行解空间中能够更加高效且均匀地搜索.实验结果表明,无论全局最优解位于搜索空间的边界区域还是中心区域,本文方法的全局搜索性能均优于现有的粒子边界处理方法.
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