A Hybrid Whale Optimization Algorithm for Global Optimization

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based (GWOA-TEO) to enhance global capability. First, high-quality initial population generated improve performance of GWOA-TEO. Then, (TEO) applied exploitation performance. Next, memory considered can store historical best-so-far solutions, achieving higher without adding additional computational costs. Finally, crossover operator based on position update mechanism leading solution are proposed exploration The GWOA-TEO then compared with five state-of-the-art algorithms CEC 2017 benchmark test functions 8 UCI repository datasets. statistical results show has good accuracy for optimization. classification datasets also competitive regard comparison in recognition rate. Thus, proven execute excellent solving problems.