Order whale optimization algorithm in rendezvous orbit design

Jiang, Ruiye; Chao, Tao; Wang, Songyan; Yang, Ming

doi:10.1109/icaci.2018.8377588

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Author and Reference Methods to Generate Initial Population Ruiye et al [33] Latin hyper cube sampling Zhang and Liu [46] Good point set method Yin et al [47] Logistic chaotic map Jianhao et al [48] Chaotic map/Opposition based learning Alamri et al [61] Opposition based learning Abd Elaziz and Mirjalili [62] Differential evolution Chaotic map/Opposition based learning Chen et al [63] Logistic chaotic map Chen [64] Bernoulli shift map Fan et al [65] Tent chaotic map Ning and Cao [66] Good point set method Jin et al [67] Random gaussian distribution…”

Section: Methods To Generate Initial Populationmentioning

confidence: 99%

“…Another simple approach was introduced in [32] where distance (D)'s calculation is modified for proper balance between diversification and intensification. In exploration, one component of each whale is changed with a random value with a probability p. Ruiye et al [33] attempted to change both exploration and exploitation phases Initial Population is generated with Latin Hypercube sampling method, then fitness value is calculated and sorted, and best individual is selected as the best solution. New parameter k and W are also used to further update whales' position.…”

Section: Improved Learning Strategymentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive survey of whale optimization algorithm: modifications and classification

Mahmood

Bawany

Tanweer

2023

IJEECS

View full text Add to dashboard Cite

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.

show abstract