RIME: A physics-based optimization
Hang Su, Dong Zhao, Ali Asghar Heidari, Lei Liu, Xiaoqin Zhang, Majdi Mafarja, Huiling Chen
Neurocomputing, 2023
This paper proposes an efficient optimization algorithm based on the physical phenomenon of rime-ice, called the RIME or rime optimization algorithm. The RIME algorithm implements the exploration and exploitation behaviors in the optimization 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 experiments 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
RIME is an efficient optimization algorithm based on the physical phenomenon of rime-ice that can be used for any class of problems. You just use the codes in your software, add your objective function, and try it.
1) A novel meta-heuristic algorithm based on natural phenomena, called the rime optimization algorithm, is inspired by the growth of rime-ice.
2) A new exploration strategy, exploitation mechanism, and selection mechanism are constructed in the RIME algorithm, and each strategy is portable and can be used to improve peer algorithms.
3) Through qualitative analysis experiments and parameter sensitivity experiments, the algorithmic characteristics of RIME are detailed for more relevant application to various optimization problems.
4) A comparison experiment between RIME and 20 peer algorithms is designed based on the complete data set. The experimental results confirm that the RIME has a tremendous advantage over peer algorithms in terms of optimal performance in various types of problems.
5) The RIME algorithm is applied to five practical engineering optimization problems, which initially demonstrates the algorithm's potential for application to practical optimization problems and can be subsequently used on other optimization problems.
The PDF files of the RIME paper is available for download
The paper is online in Elsevier in this link
RIME: A physics-based optimization
Hang Su, Dong Zhao, Ali Asghar Heidari, Lei Liu, Xiaoqin Zhang, Majdi Mafarja, Huiling Chen
Neurocomputing, 2023
Designed by
Ali Asghar Heidari