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Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

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Abstract

The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.

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Authors and Affiliations

  1. School of Information and Communication Technology, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

  2. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, China

    Gai-Ge Wang

  3. Industrial and Systems Engineering Graduate Program (PPGEPS), Pontifical Catholic University of Parana (PUCPR), Curitiba, Parana, Brazil

    Leandro dos S. Coelho

  4. Electrical Engineering Graduate Program (PPGEE), Department of Electrical Engineering, Polytechnic Center, Federal University of Parana (UFPR), Curitiba, Parana, Brazil

    Leandro dos S. Coelho

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  1. Seyedali Mirjalili
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  2. Gai-Ge Wang
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  3. Leandro dos S. Coelho
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Correspondence to Seyedali Mirjalili.

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Mirjalili, S., Wang, GG. & Coelho, L.S. Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput & Applic 25, 1423–1435 (2014). https://doi.org/10.1007/s00521-014-1629-6

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  • DOI: https://doi.org/10.1007/s00521-014-1629-6

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