Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

lu, Adil Baykaso ̆; Özbakır, Lale; Tapkan, Pınar

doi:10.5772/5101

Cited by 155 publications

(101 citation statements)

References 48 publications

(46 reference statements)

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“…The Smart bee can memorize the location of the best food source and its quality which was found at previous times [11]. This modified SB algorithm was tested on standard benchmark functions for constrained optimization problems and proved to be superior.…”

Section: Related Workmentioning

confidence: 99%

Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm

Nwiabu¹,

Okpomo²

2018

IJCA

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In Nigeria, there have been increases in damages due to fire outbreaks particularly in industrial and busy environments. Fire outbreak has caused serious injuries to people, loss of lives, damage of properties etc. Methods usually used in predicting fire outbreaks are fire alarm, flame detection, smoke detection algorithm, real-time fire, flame detection etc. This Research work introduces an artificial bee colony heuristic for predicting fire outbreaks in industrial environment in Nigeria. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. In this paper, artificial bee colony technique was used for predicting fire outbreaks caused by electrical faults. Two Experiments were conducted, the first Experiment (Exp. 1) using 26 different test simulations was performed using different fault resistance, a constant colony size of 20 (area of search) and max Cycles of 5 (maximum number of iteration). It shows that when the fault resistance is between 0.3 ohms -0.0 ohms, there will be likelihood of danger occurring among all faults at the same time, and none of the faults will be normal. While the second Experiment (Exp. 2) conducted, using 26 different test simulations was performed using different fault resistance, a constant colony size of 100 (area of search) and max Cycles of 50 (maximum number of iteration), it proves that when the fault resistance is between 0.4 ohms -0.0 ohms, there will be likelihood of danger occurring among all faults at the same time. The results also prove good performance of the predictive ABC system for average convergence at 2.25 at 26 trials and its unique capability to make multiple predictions. The system was simulated and modeled using Matlab 7.5.0(R2007b) program.

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Section: Related Workmentioning

confidence: 99%

Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm

Nwiabu¹,

Okpomo²

2018

IJCA

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“…A review of literature on algorithms inspired by the behaviour of bees [51] suggests that the topic is evolving and that there is no consensus on a single descriptive title for algorithms based on bees' behaviour. In literature, it is possible to find several bee inspired algorithms that use different algorithm models: Bee System, BeeHive, Virtual Bee algorithm, Bee Swarm Optimisation, Bee Colony Optimisation, Artificial Bee Colony, Bees algorithm and Honey Bees Mating Optimisation algorithm.…”

Section: Qbeamentioning

confidence: 99%

“…Currently, researchers are studying the behaviour of social insects in an effort to use the SI concepts to create algorithms with the ability to explore the solution search space of the problem in a way similar to the behaviour of social insects [51,52,53]. Some algorithms inspired by the behaviour of insects can be called meta-heuristic algorithms, because they provide a high-level framework, which can be adapted to solve optimisation, search, and related problems, as opposed to providing a stringent set of guidelines to solve a particular problem.…”

Section: Qbeamentioning

confidence: 99%

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Bernardino¹,

Bernardino²,

Sánchez‐Pérez³

et al. 2012

IJCIS

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In the last decades, nature-inspired algorithms have been widely used to solve complex combinatorial optimisation problems. Among them, Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) algorithms have been extensively employed as search and optimisation tools in various problem domains. Evolutionary and Swarm Intelligent algorithms are Artificial Intelligence (AI) techniques, inspired by natural evolution and adaptation. This paper presents two new nature-inspired algorithms, which use concepts of EAs and SI. The combination of EAs and SI algorithms can unify the fast speed of EAs to find global solutions and the good precision of SI algorithms to find good solutions using the feedback information. The proposed algorithms are applied to a complex NP-hard optimisation problem -the Terminal Assignment Problem (TAP). The objective is to minimise the link cost to form a network. The proposed algorithms are compared with several EAs and SI algorithms from literature. We show that the proposed algorithms are suitable for solving very large scaled problems in short computational times.

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“…Many genetic algorithms have been used for solving the generalized assignment problem, e.g., [13]- [15]. Other meta-heuristic algorithms involved to the generalized assignment problem are tabu search algorithm in [16], and ant colony optimization algorithm in [17].…”

Section: International Journal Of Applied Physics and Mathematicsmentioning

confidence: 99%

Efficient Heuristics for Single-Source Transportation Problems

Pongchairerks¹

2014

IJAPM

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This paper develops two efficient heuristics to solve the single-source transportation problem, i.e. the special class of the transportation problem where each customer must be served by only one supplier. The proposed heuristics are both started by simplifying the problem into the simpler transportation problem where each customer requires exact one product lot. Then, the first heuristic solves the simplified problem using the transportation algorithm while the second heuristic solves it using the assignment algorithm. The heuristics return an optimal solution or a near-optimal solution for every benchmark instance.

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Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

Cited by 155 publications

References 48 publications

Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm

Predicting Fire Outbreak Caused by Electrical Faults using Artificial Bee Colony Algorithm

Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Efficient Heuristics for Single-Source Transportation Problems

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