LSEWOA: An Enhanced Whale Optimization Algorithm with Multi-Strategy for Numerical and Engineering Design...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Whale Optimization Algorithm (WOA) is a bio-inspired metaheuristic known for its simplicity but suffers from premature convergence and an exploration-exploitation imbalance.
• Existing WOA variants struggle with population diversity and convergence accuracy in later iterations.

Purpose of the Study:

• To propose a novel multi-strategy enhanced Whale Optimization Algorithm (LSEWOA).
• To address the limitations of the classic WOA, including premature convergence and poor exploration-exploitation balance.

Main Methods:

• Introduced Good Nodes Set Initialization for uniform whale distribution.
• Developed a Leader-Followers Search-for-Prey Strategy and a Spiral-based Encircling Prey strategy.
• Implemented an Enhanced Spiral Updating Strategy and redesigned the convergence factor update mechanism.

Main Results:

• LSEWOA demonstrated superior performance on the CEC2005 benchmark functions.
• Quantitative analysis showed LSEWOA outperforming state-of-the-art algorithms across various dimensions (30, 50, 100).
• Successful application to nine engineering design optimization problems validated real-world applicability.

Conclusions:

• LSEWOA effectively overcomes the shortcomings of the classic WOA.
• The proposed enhancements significantly improve convergence rate, accuracy, and population diversity.
• LSEWOA offers a robust and efficient solution for complex optimization challenges.