Search methods using the algorithms: particle swarm and genetic
DOI:
https://doi.org/10.21501/21454086.1901Keywords:
Particle swarm algorithm, genetic algorithm, simulation, optimization, comparisonAbstract
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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