Application of spiral enhanced whale optimization algorithm in solving optimization problems.

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization Algorithms
• Swarm Intelligence

Background:

• The Whale Optimization Algorithm (WOA) is a widely used metaheuristic but faces limitations in population diversity, exploration-exploitation balance, and solution accuracy.
• Addressing these limitations is crucial for advancing optimization techniques.

Purpose of the Study:

• To propose the Spiral-Enhanced Whale Optimization Algorithm (SEWOA) by integrating novel strategies into the original WOA.
• To enhance the performance of WOA in terms of population diversity, exploration-exploitation balance, and solution accuracy.

Main Methods:

• Incorporation of an Archimedean spiral structure to increase solution space diversity and escape local optima.
• Implementation of a nonlinear time-varying self-adaptive dynamic perturbation strategy to improve local search capability and solution accuracy.
• Validation using CEC2014, CEC2017, and 23 benchmark test functions, plus three engineering design problems.

Main Results:

• SEWOA demonstrated significant improvements in population diversity compared to the original WOA.
• The enhanced algorithm achieved a better balance between global exploration and local exploitation.
• SEWOA exhibited superior solution accuracy across various benchmark functions and engineering problems.

Conclusions:

• The proposed SEWOA effectively overcomes the limitations of the standard WOA.
• SEWOA offers enhanced performance in diversity, balance, and accuracy, making it a valuable tool for complex optimization tasks.
• The algorithm shows strong potential for practical applications in engineering design and other fields.