Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Meta-heuristic Techniques

Background:

• The Grey Wolf Optimizer (GWO) is an effective meta-heuristic algorithm.
• GWO suffers from premature convergence and entrapment in local optima due to its reliance on the alpha wolf.
• Lack of population diversity during stagnation limits GWO's global search capability.

Purpose of the Study:

• To enhance the Grey Wolf Optimizer (GWO) algorithm for improved numerical optimization.
• To address the limitations of GWO, specifically local optima entrapment and population diversity.
• To introduce a novel algorithm, CMWGWO, incorporating Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID).

Main Methods:

• Integration of Chaotic Opposition Learning (COL) to intensify diversification and reduce stagnation.
• Implementation of Mirror Reflection Strategy (MRS) to expand exploration range and enhance global search.
• Incorporation of Worst Individual Disturbance (WID) for improved information exchange and escape from local optima.

Main Results:

• CMWGWO demonstrated superior performance compared to the original GWO.
• The enhanced algorithm showed improved convergence accuracy and robust optimization capabilities.
• Experiments were conducted on 23 benchmark functions, 10 CEC19 functions, and 3 engineering problems across various dimensions.

Conclusions:

• CMWGWO effectively overcomes the limitations of the standard GWO algorithm.
• The proposed enhancements significantly improve exploration potential and search precision.
• CMWGWO offers a more effective and robust solution for numerical optimization challenges.