A comprehensive survey of whale optimization algorithm: modifications and classification

Abstract

Whale optimization algorithm (WOA) is an emerging nature-inspired, swarm-intelligence based algorithm to solve optimization problems more efficiently. This algorithm is based on the bubble-net hunting strategy of the humpback whales. It has gained immense popularity among researchers, typically, due to its simple nature, fast convergence, and having minimum parameters. In the recent past, it has been widely adopted in various fields including data mining, machine learning, wireless sensor networks, cloud computing, civil engineering, and power systems due to its optimal performance. The WOA has given competitive results in comparison to the state-of-the-art optimization algorithms. In this study, we aim to present a comprehensive survey of WOA consisting of more than eighty existing variants of WOA. More specifically, we intend to put forward key aspects of WOA variants with reference to modifications and applications. Further, we classify the most dominant variants of WOA in distinct categories based on modification area such as equation modification, parameter tuning or the problem space for which an algorithm has been specifically altered. We believe that this study will be beneficial for the community working on optimization problems and it can serve as a basis for understanding the modification and improvement process of an optimization algorithm.

An improve unsupervised discretization using optimization algorithms for classification problems

“…The integration of discretization with WOA proves influential in solving diverse optimization challenges in domains such as classification [20]. This approach harnesses the power of discretization techniques to convert continuous variables into discrete states, optimizing them efficiently with WOA in applications across various domains [21]− [23].…”

A novel particle swarm optimization-based intelligence link prediction algorithm in real world networks

“…Several problems have been solved using swarm intelligence based techniques and presented in the current year [28]- [31]. A comprehensive survey of whale optimization algorithm was presented by Mahmood et al [32] recently. Applications of PSO have been observed in different fields very recently [33]- [36].…”

Comparative study of nature-inspired maximum power point tracking algorithms for partially shaded photovoltaic systems

“…Conventional algorithms designed for MPPT perform well in situations where solar conditions are consistent, but they struggle when faced with partial shading or rapidly changing environmental conditions [20]. To address these challenges, researchers have turned to nature-inspired algorithms, such as particle swarm optimization (P&O) [21], [22], ant colony optimization (ACO) [23], artificial bee colony (ABC), whale optimization [24], and differential evolution (DE). These nature-inspired algorithms excel in global search problems and demonstrate effectiveness across diverse environmental conditions.…”