Abstract
Nowadays, there are many tools to solve the optimization problem. One of the popular tool is the population-based metaheuristics can be viewed as an iterative improvement in a population of solutions. Algorithms such as Particle swarm optimization (PSO) is the swarm intelligent that find the answer by global and local search with the velocity and genetic algorithm (GA) is the stochastic search procedure based on the mechanics of natural selections. Both of them belong to this class of metaheuristics. In this paper is to present the perspective and experiments of the hybrid algorithm of genetic algorithm and particle swarm optimization to solve the optimization problems.
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This project was supported by the Theoretical and Computational Science (TaCS) Center under Computational and Applied Science for Smart Innovation Cluster (CLASSIC), Faculty of Science, KMUTT.
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Sombat, A., Saleewong, T., Kumam, P. (2018). Perspectives and Experiments of Hybrid Particle Swarm Optimization and Genetic Algorithms to Solve Optimization Problems. In: Anh, L., Dong, L., Kreinovich, V., Thach, N. (eds) Econometrics for Financial Applications. ECONVN 2018. Studies in Computational Intelligence, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-73150-6_23
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DOI: https://doi.org/10.1007/978-3-319-73150-6_23
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