Improved Energy Valley Optimizer with Levy Flight for Optimization Problems

Nabila H Shikoun; Islam S Fathi

doi:10.61927/igmin172

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André Mampuya Nzita, Edmond Phuku Phuati, Robert Muanda Ngimbi, Guyh Dituba Ngoma and Nathanaël Masiala Mavungu

Engineering Group Research Article Article ID: igmin172

Improved Energy Valley Optimizer with Levy Flight for Optimization Problems

Data Engineering Artificial Intelligence Machine Learning DOI10.61927/igmin172

Nabila H Shikoun ^1,2 and

Islam S Fathi ^* ¹

Affiliation

¹Department of Information Systems, Al Alson Higher Institute, Cairo 11762, Egypt

²Department of Systems and Computer Engineering, Faculty of Engineering, Alazhar University, Cairo, Egypt

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Abstract

Energy Valley Optimizer (EVO) is one of the recent metaheuristic algorithms. It draws inspiration from advanced principles in physics related to particle stability and decay modes. This paper presents a new Energy Valley Optimizer (EVO) and levy flights that are hybrid to improve the EVO in solving optimization problems. Levy flight is one of the most important randomization techniques. Fifteen mathematical test functions (five unimodal functions, four multimodal functions, and six composite functions) are solved with the proposed algorithm. We also compare our results with previous results of metaheuristic algorithms. The statistical results show that the results of the Levy Energy Valley Optimizer (LEVO) outperform other algorithms in almost all mathematical test functions.

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References

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