An enhanced Kepler optimization algorithm with global attraction model and dynamic neighborhood search for global optimization and engineering problems

Abstract

The Kepler optimization algorithm (KOA) is a recently proposed physics-based algorithm inspired by Kepler’s laws. Despite the strong competitiveness of KOA relative to established algorithms, it faces challenges such as limited search capability, premature convergence, and low convergence accuracy in solving complex optimization problems. To address these shortcomings, we propose an enhanced KOA (EKOA) that integrates a global attraction model, a dynamic neighborhood search operator, and a local update strategy with multi-elite guided differential mutation. Firstly, EKOA introduces an innovative global attraction model to facilitate information exchange among individuals, aiming to extend the search space and improve search efficiency. Secondly, a dynamic neighborhood search operator is designed to weaken the influence of the best individual on the current position updates, thereby mitigating premature convergence. Finally, a local update strategy with multi-elite guided differential mutation is developed to provide new evolutionary opportunities for individuals, ensure evolution in a more favorable direction, and prevent stagnation of the optimal solution during the optimization process. The performance of EKOA is evaluated by comparing it with 12 state-of-the-art algorithms using the CEC2017, CEC2020, and CEC2022 benchmark test suites. Experimental results and statistical analysis substantiate the superiority of EKOA. Additionally, the practical applicability of EKOA is demonstrated through four real-world engineering problems. In conclusion, EKOA not only effectively enhances the performance of the original KOA but also emerges as a powerful and promising algorithm for solving complex engineering problems.