Artificial Bee Colony Algorithm with Uniform Mutation

Singh, Anushikha; Gupta, Nitesh; Sinhal, Amit

doi:10.1007/978-81-322-0487-9_49

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…In the enhanced ABC algorithm (E-ABC), the genetic algorithm (GA) operators of crossover [22] and mutation [23] are included in the original ABC algorithm. The main steps of the algorithm are:…”

Section: Artificial Bee Colony Algorithm: the Enhanced Versionmentioning

confidence: 99%

An optimized multiobjective CPU job scheduling using evolutionary algorithms

Venkatraman¹,

Selvagopal²

2018

Turk J Elec Eng & Comp Sci

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Scheduling in a multiprocessor parallel computing environment is an NP-hard optimization problem. The main objective of this work is to obtain a schedule in a distributed computing system (DCS) environment that minimizes the makespan and maximizes the throughput. We study the use of two of the evolutionary swarm optimization techniques, the firefly algorithm and the artificial bee colony (ABC) algorithm, to optimize the scheduling in a DCS. We also enhance the traditional ABC algorithm by merging the genetic algorithm techniques of crossover and mutation with the employed bee phase and the onlooker phase, respectively. The resulting enhanced ABC algorithm is used as the scheduling algorithm and is evaluated against the firefly and ABC algorithms. The results obtained show that in a distributed environment with a large number of jobs and resources, multiobjective scheduling using evolutionary algorithms can perform well in terms of minimizing makespan and maximizing throughput.

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Section: Artificial Bee Colony Algorithm: the Enhanced Versionmentioning

confidence: 99%

An optimized multiobjective CPU job scheduling using evolutionary algorithms

Venkatraman¹,

Selvagopal²

2018

Turk J Elec Eng & Comp Sci

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“…In paper [9], one more phase in the form of mutation operator is added to original Artificial Bee Colony algorithm. In standard ABC algorithm, there are only 4 phases that described the overall working of this algorithm, but here one additional phase after the scout bee phase of ABC algorithm is added in the form of Mutation operator.…”

Section: Artificial Bee Colony Algorithm With Mutationmentioning

confidence: 99%

Incremental Enhanced Artificial Bee Colony Algorithm with Local Search

Pathak¹,

Mishra²,

Kushwah³

2015

IJCA

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Swarm intelligence is the collective problem solving behavior of groups of artificial agents. These agents local interaction with each other can be negative, positive or neutral. Here positive interaction helps agents to solve a problem while negative interaction block the agents for solving problem and neutral interaction does not affect the swarm"s performance. In this work, incremental enhanced ABC algorithm with local search is used for reducing negative interaction without complexifying the agent"s behavior. Here in the enhanced artificial bee colony algorithm, one additional phase in the form of mutation operator is used. With the help of mutation operator, algorithm may not be trapped into local optima due to the chance of changing local best position.The experimental results show that the performance of proposed algorithm and the proposed algorithm is compared with standard ABC algorithm, Artificial Bee Colony algorithm with mutation algorithm.

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“…In this Work [11], one additional step, i.e. mutation operator, is added to standard artificial bee colony algorithm.…”

Section: Different Modified Version Of Abc Algorithms 21 Abc Algoritmentioning

confidence: 99%

Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation

Maheshwari¹,

Datta²

2014

IJCA

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Swarm intelligence systems are made up of a population of simple agents interacting locally with each another and with their environment. Artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE) etc, are some example of swarm intelligence. In this work, an efficient modified version of ABC algorithm is proposed, where two additional operator crossover and mutation operator is used in the ABC algorithm. Here Crossover operator is used after the employed bee phase and mutation operator is used after scout bee phase of ABC algorithm. Proposed algorithm is applied at standard travelling salesman problem (TSP) for checking the efficiency of proposed algorithm and also simulated results are compared with ABC with uniform mutation algorithm and Basic ABC algorithm. The simulated result showed that the proposed algorithm is better than all the modified version of ABC algorithm.

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Artificial Bee Colony Algorithm with Uniform Mutation

Cited by 12 publications

References 7 publications

An optimized multiobjective CPU job scheduling using evolutionary algorithms

An optimized multiobjective CPU job scheduling using evolutionary algorithms

Incremental Enhanced Artificial Bee Colony Algorithm with Local Search

Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation

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