GWOA: A multi-strategy enhanced whale optimization algorithm for engineering design optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Traditional Whale Optimization Algorithm (WOA) suffers from local optima, slow convergence, and limited global search for high-dimensional problems.
• Existing WOA variants may not fully address these limitations in complex optimization scenarios.

Purpose of the Study:

• To propose an improved Whale Optimization Algorithm (GWOA) addressing the shortcomings of the traditional WOA.
• To enhance global search ability, convergence speed, and solution accuracy for complex optimization problems.

Main Methods:

• Integration of adaptive parameter adjustment, enhanced prey encircling, and sine-cosine search strategies into WOA.
• Testing GWOA on 23 benchmark functions (unimodal, multimodal, compositional) and engineering design problems (pressure vessel, spring).
• Comparative analysis with basic metaheuristics, WOA variants, and state-of-the-art algorithms, including scalability experiments.

Main Results:

• GWOA demonstrated superior convergence speed and solution accuracy across most benchmark functions, particularly for multimodal and compositional problems.
• Achieved an Overall Efficiency (OE) of 74.46% in benchmark tests.
• Effectively reduced costs and met constraints in engineering optimization problems, showing enhanced stability and optimization ability.

Conclusions:

• GWOA significantly improves global search, convergence speed, and solution stability through multi-strategy integration.
• GWOA shows great potential for solving complex optimization problems and serves as an efficient tool for engineering applications.
• The proposed GWOA offers a robust and effective alternative to traditional optimization algorithms.