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An improved binary artificial bee colony algorithm
2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE), 2017The xor-based artificial bee colony algorithm, called as binABC, is a novel variant of basic artificial bee colony (ABC) algorithm, which is proposed for solving binary optimization problems. This algorithm uses xor logic operator to search solution space instead of subtraction-based solution update rule of basic ABC due to discrete nature of the ...
Kaya, Ersin, Kiran, Mustafa Servet
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Dynamic Swarm Artificial Bee Colony Algorithm
International Journal of Applied Evolutionary Computation, 2012Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems.
Harish Sharma +3 more
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Fully informed artificial bee colony algorithm
Journal of Experimental & Theoretical Artificial Intelligence, 2015The Gbest-guided artificial bee colony (GABC) algorithm is a latest swarm intelligence-based approach to solve optimisation problem. In GABC, the individuals update their respective positions by drawing inspiration from the global best solution available in the current swarm.
Kavita Sharma 0002 +2 more
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Artificial Bee Colony Algorithm
2019Swarm intelligence and group behavior of honey bees was the inspiration basis of some metaheuristics. The first one is the Artificial Bee Colony (ABC) algorithm which was introduced by Karaboga in 2005 [1] based on the foraging behavior of honey bees. Other algorithms such as bee colony optimization [2] and bees algorithm [3] were also developed which ...
Ali Kaveh, Taha Bakhshpoori
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Memetic search in artificial bee colony algorithm
Soft Computing, 2013Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over benchmark as well as real world optimization problems.
Jagdish Chand Bansal +3 more
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Parameter Tuning for the Artificial Bee Colony Algorithm
2009While solving a problem by an optimization algorithm, adjusting algorithm parameters have significant importance on the performance of the algorithm. A fine tuning of control parameters is required for most of the algorithms to obtain desired solutions.
Bahriye Akay, Dervis Karaboga
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A multi-objective artificial bee colony algorithm
Swarm and Evolutionary Computation, 2012Abstract This work presents a multi-objective optimization method based on the artificial bee colony, called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid-based approach to adaptively assess the Pareto front maintained in an external archive.
Reza Akbari +3 more
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Artificial Bee Colony Algorithm with Uniform Mutation
2012Swarm intelligence systems are typically made up of a population of simple agents or bodies interacting locally with one another and with their environment. Particle swarm, Ant colony, Bee colony are examples of swarm intelligence. In the field of computer science and operations research, Artificial Bee Colony Algorithm (ABC) is an optimization ...
Amit Singh, Neetesh Gupta, Amit Sinhal
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Artificial bee colony algorithm on distributed environments
2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC), 2010Artificial Bee Colony (ABC) is a metaheuristic approach in which a colony of artificial bees cooperates in finding good solutions for numerical optimization problems. ABC is adopted widely for use in several domains of solution optimization. However, the algorithm generally requires a considerably large computational time and resources.
Anan Banharnsakun +2 more
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A Clustering-Based Artificial Bee Colony Algorithm
2016An advanced Artificial Bee Colony (ABC) algorithm based on fuzzy C-means (FCM) clustering method is presented in this paper, aiming to make a balance between the exploitation and exploration. Firstly, FCM method is employed to divide the population into subpopulations, so that individuals only interact with those in the same subpopulation. Furthermore,
Ming Zhang +3 more
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