An improved gray wolf optimization algorithm solving to functional optimization and engineering design problems.

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization
• Engineering Applications

Background:

• The Gray Wolf Optimizer (GWO) is a popular metaheuristic algorithm inspired by wolf pack behavior.
• Traditional GWO faces limitations in convergence speed, solution accuracy, and escaping local minima.
• Enhancements are needed to broaden GWO's applicability in complex engineering optimization.

Purpose of the Study:

• To propose a multi-strategy fusion improved Gray Wolf Optimizer (IGWO).
• To enhance GWO's convergence speed, solution accuracy, and ability to escape local minima.
• To evaluate IGWO's performance on benchmark and engineering problems.

Main Methods:

• Implemented lens imaging reverse learning for initial population optimization.
• Introduced a nonlinear cosine-variation-based control parameter strategy.
• Integrated nonlinear parameter tuning and position correction inspired by Tunicate Swarm Algorithm (TSA) and Particle Swarm Optimization (PSO).

Main Results:

• IGWO demonstrated improved exploration and exploitation (E&P) capabilities.
• Statistical analyses (Wilcoxon rank sum, Friedman tests) confirmed IGWO's balanced performance.
• IGWO showed a significant advantage over existing state-of-the-art algorithms on tested problems.

Conclusions:

• The proposed IGWO algorithm effectively addresses limitations of the traditional GWO.
• IGWO offers a robust and efficient solution for various optimization challenges in engineering.
• The multi-strategy fusion approach provides a significant advancement in metaheuristic optimization.