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

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.