HWOA-TTA: A New Hybrid Metaheuristic Algorithm for Global Optimization and Engineering Design Applications

Abstract

Hybrid metaheuristics is one of the most exciting improvements in optimization and metaheuristic algorithms. A current research topic combines two algorithms to provide a more advanced solution to optimization problems. The present study applies a new approach called HWOA-TTA which means a hybrid of the whale optimizer algorithm (WOA) and tiki-taka algorithm (TTA). The hybrid WOA-TTA combines the exploitation phase of WOA with the exploration phase of TTA. Two stages in the hybridized model are suggested. First, the WOA exploitation phase incorporates the TTA mechanism. Second, a new approach is included in the research phase to enhance the result with each iteration to a set of unconstrained benchmark test functions and engineering design applications. To verify the performance of the improved algorithm, thirteen benchmark functions have been used to compare HWOA-TTA with the classical intelligent population algorithms (PSO, TTA, and WOA). The hybrid algorithm is applied to two well-known engineering mathematical models. The experiments show that the HWOA-TTA outperforms other algorithms.