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

Hongmin Chen1, Zhuo Wang1, Heming Jia1

  • 1Department of Information Engineering, Sanming University, Sanming 365004, China.

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Summary
This summary is machine-generated.

This study introduces a hybrid optimization algorithm combining the slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), enhancing performance on complex functions. The novel approach improves convergence accuracy and global search efficiency for meta-heuristic optimization.

Keywords:
arithmetic optimization algorithmmutation strategyrandom center solution strategyrestart strategyslime mold algorithm

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Slime mold algorithm (SMA) excels in global search but struggles with late-stage oscillations.
  • Arithmetic optimization algorithm (AOA) offers strong randomness and convergence via multiplication/division operators.
  • Existing algorithms face challenges in complex function optimization and finding optimal positions.

Purpose of the Study:

  • To develop a hybrid optimization algorithm integrating SMA and AOA.
  • To enhance global search efficiency, population diversity, and convergence accuracy.
  • To address the limitations of individual algorithms in complex optimization tasks.

Main Methods:

  • Integration of SMA and AOA, replacing SMA's convergence stage with AOA's operators.
  • Incorporation of a random central solution strategy for improved global search and diversity.
  • Implementation of restart and mutation strategies to boost convergence accuracy and late-stage optimization.

Main Results:

  • The hybrid algorithm (RCLSMAOA) demonstrated effectiveness in comparative experiments on various test functions.
  • Statistical tests (Wilcoxon rank sum, Friedman) confirmed the improved performance.
  • The enhanced algorithm showed superior convergence accuracy and optimization ability compared to individual methods.

Conclusions:

  • The proposed hybrid slime mold and arithmetic optimization algorithm (RCLSMAOA) is effective for complex optimization problems.
  • The integration of random central learning, mutation, and restart strategies significantly improves performance.
  • The algorithm shows promise for application in practical engineering problems.