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Multi-Strategy Fusion Improved Walrus Optimization Algorithm for Coverage Optimization in Wireless Sensor Networks.

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Summary
This summary is machine-generated.

An improved Walrus Optimization (WO) algorithm (IMWO) enhances global exploration and stability by integrating Differential Evolution, LSC Mapping, and Beta Opposition-Based Learning. IMWO achieves superior performance in benchmark tests and Wireless Sensor Network coverage optimization.

Keywords:
Logistics–Sine–Cosine Mappingbeta opposition-based learningdifferential evolutionwalrus optimizationwireless sensor networks

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Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization
  • Engineering Optimization

Background:

  • The Walrus Optimization (WO) algorithm offers fast convergence but struggles with local optima and instability.
  • High-dimensional and complex engineering problems require robust optimization techniques.

Purpose of the Study:

  • To develop an improved Walrus Optimization (IMWO) algorithm addressing the limitations of the original WO.
  • To enhance global exploration, search stability, and convergence precision of the WO algorithm.

Main Methods:

  • Integration of Differential Evolution/best/1 (DE/best/1) mutation for improved exploration.
  • Application of Logistics-Sine-Cosine (LSC) Mapping to enhance search dynamics.
  • Incorporation of Beta Opposition-Based Learning (Beta-OBL) strategy for better initialization and convergence.

Main Results:

  • The IMWO algorithm achieved superior average fitness rankings (1.66 and 1.33) on CEC2017 and CEC2022 benchmark suites.
  • IMWO outperformed the original WO and six other state-of-the-art metaheuristics in benchmark evaluations.
  • In Wireless Sensor Network (WSN) coverage optimization, IMWO achieved high average coverage rates (95.86% and 96.48%).

Conclusions:

  • The proposed IMWO algorithm demonstrates significant improvements in global exploration, stability, and convergence precision.
  • IMWO proves effective and robust for solving complex real-world engineering optimization problems, such as WSN coverage.
  • The synergistic integration of DE/best/1, LSC Mapping, and Beta-OBL enhances metaheuristic performance.