Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation

Hongmin Chen; Zhuo Wang; Heming Jia; Xindong Zhou; Laith Abualigah

doi:10.3390/biomimetics8050396

Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation

Biomimetics (Basel). 2023 Aug 28;8(5):396. doi: 10.3390/biomimetics8050396.

Authors

Hongmin Chen¹, Zhuo Wang¹, Heming Jia¹, Xindong Zhou¹, Laith Abualigah^{2

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Affiliations

¹ Department of Information Engineering, Sanming University, Sanming 365004, China.
² Prince Hussein Bin Abdullah College for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan.
³ Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.
⁴ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan.
⁵ MEU Research Unit, Middle East University, Amman 11831, Jordan.
⁶ Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan.
⁷ School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Malaysia.

Abstract

The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult to find the optimal position in complex functions. The arithmetic optimization algorithm utilizes multiplication and division operators for position updates, which have strong randomness and good convergence ability. For the above, this paper integrates the two algorithms and adds a random central solution strategy, a mutation strategy, and a restart strategy. A hybrid slime mold and arithmetic optimization algorithm with random center learning and restart mutation (RCLSMAOA) is proposed. The improved algorithm retains the position update formula of the slime mold algorithm in the global exploration section. It replaces the convergence stage of the slime mold algorithm with the multiplication and division algorithm in the local exploitation stage. At the same time, the stochastic center learning strategy is adopted to improve the global search efficiency and the diversity of the algorithm population. In addition, the restart strategy and mutation strategy are also used to improve the convergence accuracy of the algorithm and enhance the later optimization ability. In comparison experiments, different kinds of test functions are used to test the specific performance of the improvement algorithm. We determine the final performance of the algorithm by analyzing experimental data and convergence images, using the Wilcoxon rank sum test and Friedman test. The experimental results show that the improvement algorithm, which combines the slime mold algorithm and arithmetic optimization algorithm, is effective. Finally, the specific performance of the improvement algorithm on practical engineering problems was evaluated.

Keywords: arithmetic optimization algorithm; mutation strategy; random center solution strategy; restart strategy; slime mold algorithm.

Abstract

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