Abstract
This article presents a hybrid evolutionary algorithm (HEA) based on particle swarm optimization (PSO) and a real-coded genetic algorithm (GA). In the HEA, PSO is used to update the solution, and a genetic recombination operator is added to produce offspring individuals based on the parents, which are selected in proportion to their relative fitness. Through the recombination, new offspring enter the population, and individuals with poor fitness are eliminated. The performance of the proposed hybrid algorithm is compared with those of the original PSO and GA, and the impact of the recombination probability on the performance of the HEA is also analyzed. Various simulations of multivariable functions and neural network optimizations are carried out, showing that the proposed approach gives a superior performance to the canonical means, as well as a good balance between exploration and exploitation.
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References
Eiben AE, Schippers CA (1998) On evolutionary exploration and exploitation. Fundamenta Informaticae 35(1–4):35–50
Grimaccia F, Mussetta M, Zich RE (2007) Genetic swarm optimization: self-adaptive hybrid evolutionary algorithm for electromagnetics. IEEE Trans Antennas Propagation 55(3):781–785
Kao YT, Zahara E (2008) A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput 8(2):849–857
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Piscataway, pp 1942–1948
Angeline PJ (1998) Evolutionary optimisation versus particle swarm optimisation: philosophy and performance differences. Lecture Notes in Computer Science, Springer, vol 1447, pp 601–610
Parsopoulos KE, Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1(2–3):235–306
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
Eshelman LJ, Schaffer DJ (1993) Real-coded genetic algorithms and interval schemata. In: Whitley LD (ed) Foundations of genetic algorithms, 2. Morgan Kaufmann, Los Altos, pp 187–202
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This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010
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Duong, S.C., Kinjo, H., Uezato, E. et al. Particle swarm optimization with genetic recombination: a hybrid evolutionary algorithm. Artif Life Robotics 15, 444–449 (2010). https://doi.org/10.1007/s10015-010-0846-z
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DOI: https://doi.org/10.1007/s10015-010-0846-z