Volume 12 • Issue 1 • PP: 01 –10 • 2027
A Hybrid Grey Wolf and Dipper-Throated Optimizer for Engineering Design Optimization
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© 2027 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Optimization plays a fundamental role in engineering design, enabling cost reduction, performance enhancement, and constraint satisfaction. Metaheuristic algorithms such as the Grey Wolf Optimizer (GWO) and Dipper-Throated Optimizer (DTO) have been widely used for solving complex optimization problems. However, standalone algorithms often suffer from premature convergence and limited exploration capabilities, necessitating the development of hybrid approaches. This chapter introduces a novel hybrid algorithm, GWO+DTO, which combines the exploratory strength of GWO with the exploitative efficiency of DTO to improve optimization performance. The effectiveness of GWO+DTO is evaluated on two benchmark engineering problems: the Pressure Vessel Design Problem and the Tension/Compression Spring Design Problem, comparing its results with standalone GWO and DTO. Experimental findings demonstrate that the hybrid approach achieves superior performance, obtaining the best cost of 5950.28 in the pressure vessel problem and 0.01266 in the spring design problem, outperforming the individual algorithms in accuracy and efficiency. Additionally, GWO+DTO requires fewer function evaluations, highlighting its computational efficiency. The proposed hybrid method presents a promising alternative for tackling real-world engineering optimization challenges, with potential applications in multi-objective and large-scale optimization problems.
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