Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem

Drias, Habiba; Sadeg, Souhila; Yahi, Safa

doi:10.1007/11494669_39

Cited by 113 publications

(41 citation statements)

References 2 publications

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“…Several approaches have been proposed to model the collective intelligence of honeybee [5] [ [12][13][14][15][16][17][18][19]. The honeybee swarms model consists of three components: food sources, employed foragers and unemployed foragers [20].…”

Section: Analysis Of Learning Mechanism Of Solution Generation In Artmentioning

confidence: 99%

Comparison of PSO and ABC: From A Viewpoint of Learning

Hao¹,

Wang²,

Zhang³

et al. 2017

dtcse

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Abstract. The learning mechanisms in Partial Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithm are studied. Four basic learning elements are considered, including learning subject, learning object, learning result and learning rules. Both PSO and ABC generate new solutions by learning to explore/exploit promising subspace. For the solution generation operators in each algorithm, we study the learning mechanism and analyze their exploration and exploitation ability. This study gives more insights on the similarity and differences between PSO and ABC.

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Section: Analysis Of Learning Mechanism Of Solution Generation In Artmentioning

confidence: 99%

Comparison of PSO and ABC: From A Viewpoint of Learning

Hao¹,

Wang²,

Zhang³

et al. 2017

dtcse

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show abstract

“…On the other hand bee algorithm performed slightly better than ant algorithm and the execution time for both heuristics was approximately equal. Drias et al (2005) introduced a new intelligent approach named Bees Swarm Optimization (BSO), which is inspired from the behaviour of real bees especially harvesting the nectar of the easiest sources of access while always privileging the richest. The proposed algorithm was adapted to the maximum weighted satisfiability problem (MAX-W-SAT) problem which was NP-Complete.…”

Section: Review and Categorization Of Studies On Artificial Bee Systemsmentioning

confidence: 99%

Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

lu¹,

Özbakır²,

Tapkan³

2007

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

155

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“…Notwithstanding, in recent years several other swarm intelligence algorithms have appeared, and, amongst them, those inspired by specific behaviors of honey bees, such as bees foraging [12,19] and bees mating [11]. Currently, there are a variety of algorithms inspired by the bee foraging behavior found in literature, such as: Bee System [21], Honey Bee Algorithm [17], BeeHive [29], Virtual Bee Algorithm [30], Bee Colony Optimization [27], Bees Swarm Optimization [10], Bees Algorithm [19], Honey Bee Foraging [2] and the Artificial Bee Colony Algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

Parallel Approaches for the Artificial Bee Colony Algorithm

Parpinelli

Benítez

Lopes

2011

Adaptation, Learning, and Optimization

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Abstract. This work investigates the parallelization of the Artificial Bee Colony Algorithm. Besides a sequential version enhanced with local search, we compare three parallel models: master-slave, multi-hive with migrations, and hybrid hierarchical. Extensive experiments were done using three numerical benchmark functions with a high number of variables. Statistical results indicate that intensive local search improves the quality of solutions found and, thanks to the coevolution effect, the multi-population approaches obtain better quality with less computational effort. A final comparison between models was done analyzing the trade-offs between quality of solution and processing time.

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Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem

Cited by 113 publications

References 2 publications

Comparison of PSO and ABC: From A Viewpoint of Learning

Comparison of PSO and ABC: From A Viewpoint of Learning

Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

Parallel Approaches for the Artificial Bee Colony Algorithm

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