MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach

By Hussein A. Abbass. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001

368 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/1acb8d0b80ddf70cface009fda03ef66804480de

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Citations in the corpus

Grey Wolf Optimizer (2014, background): “ Marriage in Honey Bees Optimization Algorithm (MBO) in 2001 [34]  Artificial Fish-Swarm Algorithm (AFSA) in 2003 [35]  Termite Algorithm in 2005 [36]  Wasp Swarm Algorithm in 2007 [37]  Monkey Search in 2007 [38]  Bee Collecting Pollen Algorithm (BCPA) in 2008 [39]  Cuckoo Search (CS) in 200”
The Whale Optimization Algorithm (2016, methodology): “MBO: Marriage in honey bees optimization – a haplometrosis polygynous swarming approach.”
Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm (2016, positive): “Marriage in Honey Bee Optimization Algorithm (MBO) was proposed by Abbass [26], and mimics processes of reproduction in the honey bee colony.”
A comprehensive survey: artificial bee colony (ABC) algorithm and applications (2012, methodology): “(2011), Abu-Mouti and El-Hawary (2009), Lei et al. (2010b), Ma and Lei (2010), Han et al. (2011), Baykasoğlu et al. (2007), Noaman and Jaradat (2011), Alzaqebah and Abdullah (2011a,b), Vivekanandan et al. (2011), Ji and Wu (2011), Zhang and Wu (2011a), Bolaji et al.”
Red fox optimization algorithm (2021, background): “1995 Dorigo and Di Caro (1999) Ant Colony Optimization Ants in a colony 1999 Abbass (2001) Marriage in Honey Bees Optimization Honey bees 2001 Li (2003) Artificial Fish Swarm Algorithm Fish swarm 2003 Martin and Stephen (2006) Termite Algorithm Termites in a colony 2006 Karaboga and Basturk (2007) A”
A survey on optimization metaheuristics (2013, methodology): “The main algorithm proposed based on the process of marriage in real honey-bee is the Marriage in Honey Bees Optimization algorithm (MBO) [1].”
A social spider algorithm for global optimization (2015, methodology): “Abbass proposed a Mar-riage in honey Bees Optimization (MBO) in [17] and this algorithm was applied to solve propositional satisﬁability problems (3-SAT problems).”
Chaotic bee colony algorithms for global numerical optimization (2010, methodology): “Swarm intelligence that combines of biology and social based heuristics has become a research interest to many research scientists of related fields in recent years (Abbass, 2001).”
Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems (2019, methodology): “Emperor Penguin Optimizer [41] EPO Collective Neurodynamic Optimization [1,42–44] CNO Artificial Chemical Reaction Optimization Algorithm [45] ACROA Galaxy-based Search Algorithm [46] GbSA Firefly Algorithm [47] FA Exchange Market Algorithm [48] EMA Social-Based Algorithm [49] SBA Harmony Search [50”
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “(2020) 314 Marriage in Honey Bees Optimization (MHBO) Abbass (2001) 315 Material Generation Algorithm (MGA) Talatahari et al. (2021a) 316 Mean Euclidian Distance Threshold (MEDT) Kaveh et al. (2022) 317 Meerkats Inspired Algorithm (MIA) Klein and dos Santos Coelho (2018) 318 Melody Search (MS) Ashra”
Evolutionary population dynamics and grey wolf optimizer (2014, background): “There are also a massive number of recently proposed algorithms in the literature: gravitational local search (GLSA) [1], big bang– big crunch (BBBC) [2], gravitational search algorithm (GSA) [3], central force optimization (CFO) [4], artificial chemical reaction optimization algorithm (ACROA) [5], ”
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm (2019, methodology): “Memetic algorithm Moscato 1989 2019 [20] Ant colony optimization Dorigo 1992 13,086 [21] Continuous particle swarm optimization Eberhart and Kennedy 1995 13,504 [22] Particle swarm algorithm Kennedy and Eberhart 1995 5484 [23] Binary particle swarm optimization Kennedy and Eberhart 1997 4839 [24] Ar”
A survey: algorithms simulating bee swarm intelligence (2009, methodology): “Abbass applied the model to a fifty propositional satisfiability problems (SAT) with 50 variables and 215 constraints. Abbass (2001c) developed two versions of the proposed algorithm which were incorporated with a well known heuristic for SAT. The two heuristics employed for each version are GSAT an”
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”
New inspirations in swarm intelligence: a survey (2011, methodology): “The Marriage in MBO algorithm (Section 2.2.1), was applied to several problems in the literature, for instance: three-SAT problem optimisation (Abbass, 2001; Teo and Abbass, 2003), MAX-SAT problem optimisation (Benatchba et al., 2005), water resources management (Haddad and Afshar, 2004),…”