An improved hybrid whale optimization algorithm for global optimization and engineering design problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The whale optimization algorithm (WOA) is a popular metaheuristic, but its reliance on only the best solution limits its exploration capabilities.
• Existing optimization methods often struggle to balance exploration and exploitation effectively.

Purpose of the Study:

• To introduce a novel hybrid algorithm, Pbest-guided differential WOA (PDWOA), enhancing the WOA's performance.
• To address the limitations of the standard WOA by integrating concepts from particle swarm optimization (PSO) and differential evolution (DE).

Main Methods:

• Development of the Pbest-guided differential WOA (PDWOA) algorithm.
• Comprehensive evaluation using 30-dimensional CEC 2014 benchmark functions.
• Testing on real-world engineering design problems: pressure vessel, tension/compression spring, and welded beam optimization.

Main Results:

• PDWOA demonstrated significant performance improvements over the original WOA and other recent methods.
• Statistical validation using Wilcoxon signed-rank and Friedman tests confirmed PDWOA's effectiveness.
• Successful application to complex benchmark and real-world optimization tasks.

Conclusions:

• The proposed PDWOA algorithm offers a superior hybrid approach to optimization.
• PDWOA effectively overcomes the limitations of the standard WOA, providing better exploration and exploitation.
• The study highlights the potential of hybrid metaheuristics for complex optimization challenges.