Abstract
The artificial electric field algorithm (AEFA) is a minimum and maximum value charged-based function optimization scheme that was created for continuous optimization problems in the first place. The deep-rooted AEFA is always one of the best-designed techniques for discovering optimal solutions to real-world optimization problems. Differential evolution is one of the best established optimization algorithm in the literature. This article aims to utilize the potential of AEFA and DE together to produce an efficient hybrid. Both AEFA and DE are synchronized to sum their strengths. The performance of the proposed hybrid algorithm is validated on seventeen benchmark problems including IEEE-CEC 2019 single objective unconstrained optimization problems and obtained experimental results are compared with several optimization algorithms. We used a statistical test, the Wilcoxon signed-rank test, to assess the randomness of the results produced by the suggested AEFA-DE. The experimental results suggest that the AEFA-DE has superior performance than others.
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Chauhan, D., Yadav, A. (2022). A Hybrid of Artificial Electric Field Algorithm and Differential Evolution for Continuous Optimization Problems. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_49
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DOI: https://doi.org/10.1007/978-981-19-2948-9_49
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