Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms

By Xin-She Yang. International Work-Conference on the Interplay Between Natural and Artificial Computation, 2005

359 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e639d708d475217d3b89ec987dc16e0ee4e5d968

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

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”
A survey: algorithms simulating bee swarm intelligence (2009, methodology): “…(2008c) Numerical problems Tsai et al. (2008) Generalized assignment problem Baykasoglu et al. (2007) Virtual bee algorithm (VBA) Numerical problems Yang (2005) Bees algorithm (BA) Algorithm Pham et al. (2005) Numerical problems Pham et al. (2006b) Optimizing neural networks for identification of…”
A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm (2007, methodology): “There is only one numerical optimization algorithm in the literature based on intelligent behaviour of honey bee swarm [15].”
On the performance of artificial bee colony (ABC) algorithm (2008, methodology): “In VBA, a swarm of virtual bees are generated and started to move randomly in the phase space.”
New inspirations in swarm intelligence: a survey (2011, background): “, 2004), virtual bee algorithm (Yang, 2005), bee colony optimisation (Teodorovic and Dell’Orco, 2005), bees swarm optimisation (Drias et al.”
Bat algorithm for multi-objective optimisation (2011, methodology): “In addition, metaheuristic algorithms start to emerge as a major player for multiobjective global optimization, they often mimic the successful characteristics in nature, especially biological systems (Kennedy and Eberhart, 1995; Yang, 2005; Yang, 2010a; Yang, 2010b).”
Binary grey wolf optimization approaches for feature selection (2016, background): “From the interactions between these bees results the possible solution for the optimization problem [10].”
A Brief Review of Nature-Inspired Algorithms for Optimization (2013, methodology): “[79] Monkey search Mucherino and Seref [44] Black hole Hatamlou [24] Particle swarm algorithm Kennedy and Eberhart [35] Central force optimization Formato [21] Virtual ant algorithm Yang [77] Charged system search Kaveh and Talatahari [34] Virtual bees Yang [68] Electro-magnetism optimization Cuevas”
A survey on optimization metaheuristics (2013, methodology): “Several foraging based algorithms have been proposed, such as the Bee Colony Optimiza tion Metaheuristic (BCO) [169], Artificial Bee Colony Algorithm (ABC) [142] and Virtual Bee Algorithm (VBA) [283].”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “…impossible or extremely difficult by using the classical optimization techniques (Karaboga and Akay 2009a; Nowak and Cirpka 2004; Clerc and Kennedy 2002; Trelea 2003; Dorigo et al. 1996; Ong et al. 2006; Storn and Price 1997; Yang and Deb 2010; Das et al. 2011; Yang 2005, 2009; Zhang et al. 2007).”
Chaotic bee colony algorithms for global numerical optimization (2010, methodology): “A few models have been developed to model the intelligent behaviors of honeybee swarms and applied for solving the problems (Abbass, 2001; Karaboga, 2005; Karaboga & Basturk, 2008; Pai, Yang, & Chang, 2009; Pham et al., 2006; Yang, 2005).”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “(2008) 506 Virtual Bees Algorithm (VBA) Yang (2005) 507 Virulence Optimization Algorithm (VOA) Jaderyan and Khotanlou (2016) 508 Virus Colony Search (VCS) Li et al.”
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm (2019, methodology): “In order to evaluate the computational complexity of the compared algorithms we follow the guidelines described in [40].”