Honey bee social foraging algorithms for resource allocation: Theory and application

Quijano, Nicanor; Passino, K.M.

doi:10.1016/j.engappai.2010.05.004

Cited by 44 publications

(19 citation statements)

References 55 publications

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“…The foraging problem discussed here is qualitatively different than the honey bee foraging problem (Quijano and Passino, 2007), as resources in the current model replenish much more quickly. As explained in the model section, food probability in a given square is constant.…”

Section: Introductionmentioning

confidence: 80%

An agent simulation study of a unified mechanism on individual foraging behaviour with individual scalar expectancies

Magnotti

Yılmaz

2012

Journal of Simulation

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In this study, we present a selective overview of group and individual foraging behaviour and propose an agent-based model to unify these methodologies, enabling the exploration of plausible causal mechanisms and novel hypothesis generation. Despite its popular place in behavioural science, little work has been done with the generalized matching equation as it pertains to simulation work. Our model uses psychologically justifiable and theoretically grounded models of memory for time and decision rules. The results of preliminary simulation experiments suggest that the model is sensitive to changes in pay-off density and correctly replicates Ideal Free Distributions.

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Section: Introductionmentioning

confidence: 80%

An agent simulation study of a unified mechanism on individual foraging behaviour with individual scalar expectancies

Magnotti

Yılmaz

2012

Journal of Simulation

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“…To identify the optimal location of bio-mass power plant (Veraa et al, 2010), Resource Allocation (Quijanoa and Passino, 2010), Constraint Optimization Problem (Karaboga and Akay, 2009), data Clustering in data mining (Karaboga and Ozturk, 2011) are some of the successful solutions based on ABC algorithm. The detailed honey bee mating algorithm is explained.…”

Section: Resultsmentioning

confidence: 99%

Artificial Bee Colony based Energy Aware and Energy Efficient Routing Protocol

F.¹

2012

Journal of Computer Science

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Problem statement: Network becomes very important technology in the day to day life style of modern computer era, in which the wireless communication become very popular for the last few years. In the same manner, the other wireless network, i.e., ado network is very popular among the researchers in the past few decades. Approach: The Ad hoc networks are defined as a multi-hop radio relaying and are capable of operating without the support of any fixed infrastructure. Identification of optimal route for data communication, efficient utilization of energy, clustering, providing congestion free communication, offering scalability, maintaining the Quality of Service (QoS) are few research issues in the Adhoc Network. Results: This study is focusing the review of current research and the requirements, characteristics and design of wireless ado routing protocol. Conclusion: The results of the proposed work show that the proposed swarm intelligence based algorithm will provide optimality than existing systems.

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“…The detailed honey bee mating algorithm is explained in the coming section. In [16], the authors illustrate the practical utility of the theoretical results and algorithm of honey bee algorithm, and shows that how it can solve a dynamic voltage allocation problem to achieve a maximum uniformly elevated temperature in an interconnected grid of temperature zones. In [17], the authors proposed a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) for solving continuous optimization problems.…”

Section: Introduction 2 Artificial Bee Colony Algorithmmentioning

confidence: 98%

“…Alok Singh [14] designed and implemented the ABC for leaf-constrained minimum spanning tree problem and concluded that computation time in the ABC is quite small and it completely outperforms both in terms of solution quality as well as running time. The ABC is also applied to identifying the optimal location of bio-mass power plant [15], Resource Allocation [16], Continuous Optimization Problem [17], Constraint Optimization Problem [18], Economic power dispatch [19], data Clustering in data mining [20][21] [22], and Path management in the computer network [23]. In [15], the authors used a new calculation tool to determine the optimal location, biomass supply area and power plant size that offer the best profitability for investor.…”

Section: Introduction 2 Artificial Bee Colony Algorithmmentioning

confidence: 99%

Energy Aware and Energy Efficient Routing Protocol for Adhoc Network Using Restructured Artificial Bee Colony System

Mohan

Baskaran

2011

High Performance Architecture and Grid Computing

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Abstract. Wireless communication is one of the fastest growing technologies all over the world. Especially, Adhoc Network is applied wide spread across the world in many different applications, which includes all major engineering systems, vehicular network etc...The optimal routing is an issue in the adhoc network and many researchers focused their attention and developed various methodologies which are feasible for certain situations. This paper proposes a honey bee mating algorithm for adhoc routing, which is a swarm intelligence technique, and this technique is already applied for data clustering; scheduling and resource allocation; optimization problems. The various benchmark proposed by the researcher for the artificial honey bee shows better result than the existing techniques. This paper has restructured the artificial bee colony algorithm from the initialization phase to the implementation phase, and shows better result than the existing methodology.

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Honey bee social foraging algorithms for resource allocation: Theory and application

Cited by 44 publications

References 55 publications

An agent simulation study of a unified mechanism on individual foraging behaviour with individual scalar expectancies

An agent simulation study of a unified mechanism on individual foraging behaviour with individual scalar expectancies

Artificial Bee Colony based Energy Aware and Energy Efficient Routing Protocol

Energy Aware and Energy Efficient Routing Protocol for Adhoc Network Using Restructured Artificial Bee Colony System

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