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A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems.

Sen Yang1, Linbo Zhang1, Xuesen Yang1

  • 1College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China.

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|August 25, 2023
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Summary
This summary is machine-generated.

The Arithmetic Optimization Algorithm (AOA) variant, ASFAOA, enhances optimization by integrating novel strategies to improve convergence and accuracy. ASFAOA demonstrates superior performance in benchmark tests and wireless sensor coverage problems.

Keywords:
arithmetic optimization algorithmexploration and exploitationglobal optimizationmeta-heuristic algorithmwireless sensor coverage

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Meta-heuristic Computing

Background:

  • The Arithmetic Optimization Algorithm (AOA) faces stagnation in complex problems, reducing convergence and accuracy.
  • Existing meta-heuristic algorithms require enhancements for better local exploitation and global exploration.

Purpose of the Study:

  • To propose an improved AOA variant, ASFAOA, with enhanced local exploitation and global exploration capabilities.
  • To address the limitations of the original AOA in complex optimization scenarios.

Main Methods:

  • Integration of a dual-opposite learning mechanism for enhanced population diversity.
  • Incorporation of tuna swarm optimization's spiral search strategy to escape local optima.
  • Application of an offset distribution estimation strategy for guided individual evolution.
  • Development of a modified cosine acceleration function for balanced exploration and exploitation.

Main Results:

  • ASFAOA demonstrated superior performance on the CEC 2017 benchmark functions.
  • The algorithm showed significant improvements in convergence, accuracy, and stability.
  • ASFAOA effectively solved wireless sensor coverage problems across different dimensions.

Conclusions:

  • ASFAOA significantly outperforms the original AOA and other state-of-the-art algorithms.
  • The proposed enhancements lead to improved performance in both theoretical and practical optimization tasks.
  • ASFAOA is a promising technique for addressing complex real-world optimization challenges.