Hierarchical multi step Gray Wolf optimization algorithm for energy systems optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Gray Wolf Optimization (GWO) is a metaheuristic algorithm inspired by wolf pack behavior.
• Standard GWO faces challenges like premature convergence and parameter sensitivity.
• Existing GWO variants do not fully capture the hierarchical structure of wolf packs.

Purpose of the Study:

• To introduce the Hierarchical Multi-Step Gray Wolf Optimization (HMS-GWO) algorithm.
• To address limitations of standard GWO, such as premature convergence and stagnation.
• To enhance exploration, exploitation, and solution diversity in optimization.

Main Methods:

• Developed HMS-GWO with a novel hierarchical decision-making framework.
• Mimics hierarchical wolf pack behavior with structured multi-step search processes for each wolf type (Alpha, Beta, Delta, Omega).
• Evaluated performance on a benchmark suite of 23 functions.

Main Results:

• HMS-GWO achieved 99% accuracy with a computational time of 3 seconds and a stability score of 0.9.
• Demonstrated significantly better performance compared to standard GA, PSO, MMSCC-GWO, WCA, and CCS-GWO.
• Showcased faster convergence and improved solution accuracy, mitigating premature convergence issues.

Conclusions:

• HMS-GWO effectively overcomes the limitations of standard GWO.
• The hierarchical approach enhances robustness and efficiency in solving complex optimization problems.
• HMS-GWO presents a promising alternative for various application domains requiring advanced optimization.