FOX Optimization Algorithm Based on Adaptive Spiral Flight and Multi-Strategy Fusion

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Original FOX optimization algorithm suffers from weak ergodicity, low diversity, and tendency to get stuck in local optima.
• Enhancing swarm intelligence algorithms is crucial for solving complex optimization problems.
• Initialization strategies significantly impact algorithm performance.

Purpose of the Study:

• To develop an enhanced FOX optimization algorithm addressing the limitations of the original method.
• To improve the exploration and exploitation capabilities of the FOX algorithm.
• To evaluate the performance of the enhanced algorithm on benchmark functions and engineering problems.

Main Methods:

• Initialization using a Tent chaotic map for improved ergodicity and diversity.
• Integration of inertial weight to enhance solution quality.
• Variable spiral position updating strategy for adaptive random walk.
• Incorporation of Levy flight and greedy approach for balanced global and local search.
• Fusion of multiple strategies for enhanced optimization.

Main Results:

• The enhanced FOX algorithm demonstrated superior convergence speed compared to other swarm intelligence algorithms.
• Significant improvements in solution accuracy and stability were observed.
• The algorithm showed a greater ability to escape local optima.
• Effective performance on CEC2017 benchmark test functions and engineering application optimization problems.

Conclusions:

• The enhanced FOX optimization algorithm effectively overcomes the drawbacks of the original method.
• The multi-strategy fusion approach leads to notable advancements in optimization performance.
• The upgraded algorithm offers a robust and efficient solution for complex optimization tasks.