使用粒子群算法和遗传算法的搜索方法

IF 0.1 Q4 ENGINEERING, MULTIDISCIPLINARY Revista Digital Lampsakos Pub Date : 2016-12-28 DOI:10.21501/21454086.1901
Jovani Alberto Jiménez-Builes, Rafael Esteban Arango-Sanchez, Leidy Diana Jiménez-Pinzón
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引用次数: 1

摘要

本文研究了两种基于种群的元启发式算法,比较了粒子群算法(PSO)和遗传算法(GA)两种搜索算法在求解目标为寻找最小值的最优化问题中的应用。为了开展这项研究,我们用JAVA编程语言编写了一个应用程序,其中包含了用于非线性函数求值的两种算法的实现。通过比较上述几种方法求得最优解的精度来显示本工作的结果,并以图形形式显示了结果达到最优解的演变过程。研究表明,粒子群算法比遗传算法具有更好的性能。
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Métodos de búsqueda usando los algoritmos de enjambre de partículas y genético
This article presents the study of two metaheuristic methods based in populations, the comparison between two search algorithms, the particle swarm algorithm (PSO) and genetic algorithm (GA) for solving problems whose objective is optimize always looking for the lowest value. To carry out this study, we made an application in JAVA programming language that contains the implementation of the two algorithms to be used for evaluation of nonlinear functions. The result of this work is shown by comparing the accuracy to obtain the optimal solution of the methods listed above, showing the evolution of the results in graphical form to reach the solution. From this study it can be concluded that the particle swarm optimization has a better performance than genetic algorithm.
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Revista Digital Lampsakos
Revista Digital Lampsakos ENGINEERING, MULTIDISCIPLINARY-
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