Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators | Zendy

Nebojša Bačanin | Zendy; Milan Tuba | Zendy

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Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Author(s) -

Nebojša Bačanin,

Milan Tuba

Publication year - 2012

Publication title -

studies in informatics and control

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.321

H-Index - 22

eISSN - 1841-429X

pISSN - 1220-1766

DOI - 10.24846/v21i2y201203

Subject(s) - artificial bee colony algorithm , computer science , swarm intelligence , metaheuristic , benchmark (surveying) , genetic algorithm , mathematical optimization , meta optimization , algorithm , artificial intelligence , particle swarm optimization , mathematics , machine learning , geodesy , geography

Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to unconstrained optimization problems and later it was adjusted for constrained problems as well. In this paper we introduce modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm. Modifications are based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions. We implemented our modified algorithm and tested it on 13 standard benchmark functions. The results were compared to the results of the latest (2011) Karaboga and Akay’s ABC algorithm and other state-of-the-art algorithms where our modified algorithm showed improved performance considering best solutions and even more considering

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