An improved grey wolf optimization algorithm based on scale-free network topology.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Grey Wolf Optimizer (GWO) is a popular metaheuristic algorithm known for its simplicity and speed.
• Classical GWO suffers from premature convergence and limited accuracy due to its restricted learning mechanism (alpha wolves only).
• Addressing these limitations is crucial for improving GWO's performance in complex optimization tasks.

Purpose of the Study:

• To propose an improved Grey Wolf Optimizer algorithm, termed Scale-Free Grey Wolf Optimizer (SFGWO).
• To enhance exploration capabilities and convergence accuracy compared to the classical GWO.
• To validate the effectiveness of SFGWO on benchmark functions and practical engineering problems.

Main Methods:

• Population formulation based on scale-free network topology, limiting interactions to topological neighbors.
• Introduction of a neighbor learning strategy to capture and leverage individual diversity.
• Implementation of an adaptive individual regeneration strategy to balance exploration and exploitation.

Main Results:

• SFGWO demonstrated superior performance in terms of solution accuracy and exploration capabilities.
• Experimental results on 23 classical and CEC2019 benchmark functions confirmed SFGWO's effectiveness.
• The algorithm's applicability was validated on three real-world engineering problems.

Conclusions:

• The proposed SFGWO algorithm effectively addresses the limitations of the classical GWO.
• SFGWO offers improved exploration and exploitation balance, leading to better convergence accuracy.
• The enhanced algorithm shows significant potential for solving complex optimization problems in engineering.