An improved multi-strategy beluga whale optimization for global optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Traditional beluga whale optimization (BWO) faces challenges with exploration-exploitation balance, leading to suboptimal convergence speed and accuracy.
• Global optimization and complex engineering problems require robust algorithms capable of efficient and accurate solutions.

Purpose of the Study:

• To introduce an improved beluga whale optimization (IBWO) algorithm designed to overcome the limitations of the original BWO.
• To enhance the balance between exploration and exploitation, and improve convergence accuracy and speed.

Main Methods:

• The IBWO algorithm incorporates a novel Group Action Strategy (GAS), inspired by beluga whale hunting behavior, to replace the BWO exploration phase.
• Dynamic Pinhole Imaging Strategy (DPIS) and Quadratic Interpolation Strategy (QIS) are integrated to boost global optimization capabilities, search rate, and maintain population diversity.
• Performance evaluation utilized CEC2017 and CEC2020 benchmark functions across various dimensions, alongside statistical Wilcoxon rank sum tests.

Main Results:

• Experimental results demonstrate that IBWO exhibits superior optimization performance and robustness compared to existing methods.
• Analysis of convergence curves and box plots confirms the enhanced efficiency and stability of the IBWO algorithm.
• The Wilcoxon rank sum test validated the significant improvements offered by IBWO.

Conclusions:

• The IBWO algorithm effectively addresses the limitations of BWO, offering improved performance for global optimization tasks.
• The integration of GAS, DPIS, and QIS strategies significantly enhances the algorithm's ability to solve complex optimization problems.
• IBWO proves its applicability and effectiveness in solving practical engineering problems, showcasing its robust performance.