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A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms

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Abstract

In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.

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Authors and Affiliations

  1. Department of Aircraft Electrics and Electronics, College of Aviation, Erciyes University, Kayseri, Turkey

    Pinar Civicioglu

  2. Engineering Faculty, Department of Geomatics Engineering, Erciyes University, Kayseri, Turkey

    Erkan Besdok

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  1. Pinar Civicioglu
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  2. Erkan Besdok
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Correspondence to Erkan Besdok.

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Civicioglu, P., Besdok, E. A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39, 315–346 (2013). https://doi.org/10.1007/s10462-011-9276-0

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