Dimensional Learning Strategy-Based Grey Wolf Optimizer for Solving the Global Optimization Problem.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Grey Wolf Optimizer (GWO) is a nature-inspired algorithm simulating wolf pack hierarchy for optimization.
• Standard GWO's reliance on three dominant wolves can lead to suboptimal guidance when their directions conflict.

Purpose of the Study:

• To propose a novel Grey Wolf Optimizer variant, the Dimensional Learning Grey Wolf Optimizer (DLGWO).
• To enhance the utilization of population knowledge and improve exploration capabilities in optimization.

Main Methods:

• The proposed DLGWO utilizes a Dimensional Learning Strategy (DLS) to create an exemplar wolf from dominant wolves.
• Levy flight is integrated into DLGWO to reinforce the algorithm's exploration ability.
• The DLGWO's performance is evaluated against standard GWO, GWO variants, and other metaheuristics on 23 benchmark functions and engineering problems.

Main Results:

• The DLGWO demonstrated superior performance in solving global optimization problems compared to the standard GWO and other algorithms.
• The dimensional learning strategy effectively improved the utilization of population knowledge.
• The integration of Levy flight enhanced the exploration capabilities of the proposed optimizer.

Conclusions:

• The DLGWO is an effective enhancement of the standard GWO, offering improved performance in global optimization.
• The DLGWO provides a robust framework for addressing complex optimization challenges in various applications.