Dynamic multi-strategy Grey Wolf optimizer and its applications.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Grey Wolf Optimizer (GWO) faces limitations in solution accuracy, convergence speed, and search capability.
• Existing GWO variants often struggle to balance global exploration and local exploitation effectively.

Purpose of the Study:

• To propose a novel Dynamic Multi-Strategy Grey Wolf Optimizer (DMSGWO) to overcome GWO's shortcomings.
• To enhance solution accuracy, convergence speed, and search capability in optimization problems.
• To validate the practical applicability of DMSGWO in engineering design and WSN coverage optimization.

Main Methods:

• Introduced four key strategies: nonlinear convergence factor, dynamic population grouping, random position update for exploration, and adaptive perturbation for exploitation.
• Evaluated DMSGWO against GWO and other swarm intelligence algorithms on 23 benchmark functions and the CEC2022 test suite.
• Assessed DMSGWO's performance on engineering design optimization and WSN coverage optimization problems.

Main Results:

• DMSGWO demonstrated superior solution accuracy, stability, and convergence speed compared to existing algorithms.
• Statistical tests (Friedman, Wilcoxon) confirmed DMSGWO's top-ranking overall performance.
• DMSGWO effectively handled constraints and achieved optimal objective values in engineering applications.

Conclusions:

• DMSGWO significantly improves upon the Grey Wolf Optimizer, offering enhanced performance across various optimization benchmarks.
• The proposed algorithm shows strong practical value for solving complex, real-world engineering and network optimization problems.
• DMSGWO represents a robust and effective advancement in swarm intelligence optimization techniques.