Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

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...

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Bibliographic Details
Published in:Neural computing & applications 2014-11, Vol.25 (6), p.1423-1435
Main Authors: Mirjalili, Seyedali, Wang, Gai-Ge, Coelho, Leandro dos S.
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Original Article
Probability and Statistics in Computer Science
Theoretical computing
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-014-1629-6