A Hybrid Whale Optimization Algorithm for Global Optimization

Abstract

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

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

“…These algorithms apply mathematical rules, laws of motion, and physical concepts such as quantum theory [ 22 ], optic [ 19 ], and Newton's gravitational law [ 18 ] to design effective search strategies which can effectively approximate the promising areas. To improve the search strategies of WOA, researchers hybridized it with physics-based algorithms such as the sine cosine algorithm (SCA) [ 102 ] and thermal exchange optimization (TEO) [ 114 ].…”

“…m-SDWOA can improve the exploration ability of canonical WOA using the modified mutualism phase and DE∕rand∕1∕bin mutation strategy and enhance exploitation ability using the commensalism phase, encircling prey, and bubble-net attacking methods. To improve the global search strategy of WOA, Lee et al [ 114 ] proposed the GWOA-TEO algorithm by hybridizing WOA with GA and thermal exchange optimization algorithm. The GWOA-TEO algorithm improves the exploration ability of WOA using GA and the exploitation ability using TEO.…”

Wind power interval prediction based on variational mode decomposition and the fast gate recurrent unit

“…However, the WOA has slow convergence and easily falls into the local optimum. Many scholars have optimized this method, for example, by increasing the inertial weight and improving the convergence rate Lee and Zhuo (2021); Saafan and El-Gendy (2021); Qiu et al (2021). Considering these shortcomings, the improved whale optimization algorithm (IWOA) was used in this study to optimize the GRU parameters to obtain a prediction interval with high accuracy and smaller bandwidth Zhang D. W. et al (2022).…”

An Optimal Stacked ResNet-BiLSTM-Based Accurate Detection and Classification of Genetic Disorders

“…The WOA imitates humpback whales' haunting demeanor [ 28 , 29 ]. To reflect the demeanor of the whale the algorithm consists of two phases including the exploration phase and the exploitation phase.…”