Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

Seyedali Mirjalili, Gai Ge Wang, Leandro dos S. Coelho

Research output: Contribution to journalArticle

105 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1423-1435
Number of pages13
JournalNeural Computing and Applications
Issue number6
Publication statusPublished - 14 Oct 2014
Externally publishedYes



  • Binary algorithms
  • Binary optimization
  • Gravitational search algorithm
  • Particle swarm optimization

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