A New Hybrid Improved Kepler Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications

Zhenghong Qian, Yaming Zhang (), Dongqi Pu, Gaoyuan Xie, Die Pu and Mingjun Ye
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Zhenghong Qian: School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Yaming Zhang: School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Dongqi Pu: School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Gaoyuan Xie: School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Die Pu: School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Mingjun Ye: School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China

Mathematics, 2025, vol. 13, issue 3, 1-30

Abstract: The Kepler optimization algorithm (KOA) is a metaheuristic algorithm based on Kepler’s laws of planetary motion and has demonstrated outstanding performance in multiple test sets and for various optimization issues. However, the KOA is hampered by the limitations of insufficient convergence accuracy, weak global search ability, and slow convergence speed. To address these deficiencies, this paper presents a multi-strategy fusion Kepler optimization algorithm (MKOA). Firstly, the algorithm initializes the population using Good Point Set, enhancing population diversity. Secondly, Dynamic Opposition-Based Learning is applied for population individuals to further improve its global exploration effectiveness. Furthermore, we introduce the Normal Cloud Model to perturb the best solution, improving its convergence rate and accuracy. Finally, a new position-update strategy is introduced to balance local and global search, helping KOA escape local optima. To test the performance of the MKOA, we uses the CEC2017 and CEC2019 test suites for testing. The data indicate that the MKOA has more advantages than other algorithms in terms of practicality and effectiveness. Aiming at the engineering issue, this study selected three classic engineering cases. The results reveal that the MKOA demonstrates strong applicability in engineering practice.

Keywords: Kepler optimization algorithm; good point set; normal cloud model; opposition-based learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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