4.6 Article

RIME: A physics-based optimization

Journal

NEUROCOMPUTING
Volume 532, Issue -, Pages 183-214

Publisher

ELSEVIER
DOI: 10.1016/j.neucom.2023.02.010

Keywords

Meta -heuristic; Optimization; Rime optimization algorithm; RIME; Metaheuristic; Swarm -intelligence; Nature -inspired computing; Genetic algorithm; Engineering design problems; Physics; CEC2022; CEC2017

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This paper proposes an efficient optimization algorithm called RIME, which is based on the physical phenomenon of rime-ice. The algorithm simulates the growth process of soft-rime and hard-rime of rime-ice and constructs a corresponding search strategy and puncture mechanism. It improves the greedy selection mechanism and enhances the exploitation capability of the algorithm. The experimental results demonstrate the performance advantage of RIME compared to other well-established and improved algorithms, and the algorithm shows effectiveness and competitiveness in real-world problems.
This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice, called the RIME. The RIME algorithm implements the exploration and exploitation behaviors in the optimiza-tion methods by simulating the soft-rime and hard-rime growth process of rime-ice and constructing a soft -rime search strategy and a hard-rime puncture mechanism. Meanwhile, the greedy selection mechanism in the algorithm is improved, and the population is updated in the stage of selecting the optimal solution to enhance the exploitation capability of the RIME. In the experimental, this paper conducts qualitative analysis experiments on the RIME to clarify the characteristics of the algorithm in the process of finding the optimal solution. The performance of RIME is then tested on a total of 42 functions in the classic IEEE CEC2017 and the latest IEEE CEC2022 test sets. The proposed algorithm is compared with 10 well-established algorithms and 10 latest improved algorithms to verify its performance advantage. In addition, this paper designs exper-iments for the parametric analysis of RIME to discuss the potential of the algorithm in running different parameters and handling different problems. Finally, this paper applies RIME to five practical engineering problems to verify its effectiveness and superiority in real-world problems. The statistical and comparison results show that the RIME is a strong and competitive algorithm. The source code of the RIME1 algorithm and associated files are publicly accessible at https://aliasgharheidari.com/RIME.html.(c) 2023 Elsevier B.V. All rights reserved.

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