TSWOA: An Enhanced WOA with Triangular Walk and Spiral Flight for Engineering Design Optimization

The Whale Optimization Algorithm (WOA) is a meta-heuristic algorithm based on the hunting behavior of humpback whales. Its fundamental principle is intuitive, and the structure is simple, making it easy to implement. However, WOA struggles with balancing exploration and exploitation, and the quality of the population tends to degrade in the later stage of iterations. This paper proposes a novel WOA based on multiple strategies (TSWOA) to address the drawbacks of WOA. TSWOA introduced Good Nodes Set initialization to ensure a uniformly distributed population, improving exploration efficiency. TSWOA incorporated three innovative position updating strategies: Enhanced Search-for-prey strategy, Spiral Encircling Prey strategy, and Triangular Spiral Hunting strategy, which collectively enhance optimization performance. The convergence factor a was redefined to dynamically balance global exploration and local exploitation, enabling adaptive search behavior. These improvements allowed TSWOA to achieve high convergence accuracy, faster rates, and better population diversity. TSWOA's effectiveness was validated through three tests: testing TSWOA on classical benchmark functions against other algorithms, applying TSWOA to real-world engineering design problems. The results demonstrated TSWOA's capability to address optimization challenges effectively, highlighting its potential as a powerful tool for engineering applications, particularly in design, simulation, and manufacturing tasks.