The Whale Optimization Algorithm

By Seyedali Mirjalili, Andrew Lewis. Advances in Engineering Software, 2016

12,725 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/683f68101f573d69dccbc380cf97ab9b39b21d96

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In Artificial Intelligence

In Computational Theory and Mathematics

In Computer Vision and Pattern Recognition

Citations in the corpus

Path Planning for Autonomous Mobile Robots: A Review (2021, background): “Particles [115–117,128] Best position memory, variable velocity Fireflies [129] Brightness atraction Dragonflies [130] Hunting and migration Grasshoppers [131,132] Attraction and repulsion Wolves [133,134] Hierarchy system, hunting for prey Whales [135,136] Spiral bubble-nets Cuckoos [137] Lévy flig”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “FSA Fish-Swarm Algorithm [138] FSS Fish School Search [139] GEA Group Escaping Algorithm [140] GOA Grasshopper Optimisation Algorithm [141] GSO Glow-worm Swarm Optimization [142] GWO Grey Wolf Optimizer [143] HBA Honey Bee Algorithm [144] HBF Honey Bee Foraging [145] HBMO Honey Bees Mating Optimizat”
Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts (2021, methodology): “SCA (Mirjalili, 2016), SSA (Mirjalili, et al., 2017), GWO (Mirjalili, et al., 2014), MFO (S. Mirjalili, 2015), WOA (Mirjalili & Lewis, 2016), GOA (Saremi, et al., 2017), DA (Seyedali Mirjalili, 2015b), ALO (Seyedali Mirjalili, 2015a), PSO (Kennedy & Eberhart, 1995), DE (G. Sun, Yang, Yang, & Xu, 201”
Spider wasp optimizer: a novel meta-heuristic optimization algorithm (2023, background): “2020), whale optimization algorithm (WOA) (Mirjalili and Lewis 2016), marine predators algorithm (MPA) (Faramarzi et al.”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, neutral): “The whale optimization algorithm (WOA) has two primary internal parameters that must be modified to transition from exploration to exploitation, namely A and C (Mirjalili and Lewis 2016).”
Political Optimizer: A novel socio-inspired meta-heuristic for global optimization (2020, neutral): “Nature-inspired meta-heuristic algorithms have been classiﬁed in 4 major groups in the literature: Evolution-based, swarm-based, physics-based, and human behavior-based algorithms [3, 4].”