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An improved discrete particle swarm optimization algorithm for the capacitated p-median problem

机译:改进的离散p群算法

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Capacitated p-median problem (CPMP) is a kind of location problem which belongs to combinatorial optimization problem. In this paper, an improved discrete particle swarm optimization (IDPSO) algorithm is present to solve the CPMP. In the IDPSO, the candidate particle is affected by the particle local best location (it is called as pBest) and global best position (it is called as gBest) through performing crossover operation with them; while inertia effect is considered as mutation operation. In order to further adjust median points' position, cluster idea is introduced. Secondly, some key issues, such as the particle representation, particle initial process, particle update, fitness evaluation are addressed. And then, two local search operators are applied to refine the particle; and the particle replace strategy based on simulated annealing method is used to keep the diversity of the particles. The results of the experiment show that proposed algorithm could perform well while comparing with other heuristic algorithms.
机译:容量p中值问题(CPMP)是一种位置问题,属于组合优化问题。本文提出了一种改进的离散粒子群算法(IDPSO)来解决CPMP问题。在IDPSO中,候选粒子通过与它们进行交叉操作而受到粒子局部最佳位置(称为pBest)和全局最佳位置(称为gBest)的影响;而惯性效应被认为是变异操作。为了进一步调整中点的位置,引入了聚类思想。其次,解决了一些关键问题,例如粒子表示,粒子初始过程,粒子更新,适应性评估。然后,应用两个本地搜索运算符来细化粒子。并采用基于模拟退火的粒子替换策略来保持粒子的多样性。实验结果表明,与其他启发式算法相比,该算法性能良好。

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