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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/10271
Title: Gravitational swarm optimizer for global optimization
Authors: Yadav A.
Deep, K.
Kim J.H.
Nagar A.K.
Published in: Swarm and Evolutionary Computation
Abstract: In this paper, a new meta-heuristic method is proposed by combining Particle Swarm Optimization (PSO) and gravitational search in a coherent way. The advantage of swarm intelligence and the idea of a force of attraction between two particles are employed collectively to propose an improved meta-heuristic method for constrained optimization problems. Excellent constraint handling is always required for the success of any constrained optimizer. In view of this, an improved constraint-handling method is proposed which was designed in alignment with the constitutional mechanism of the proposed algorithm. The design of the algorithm is analyzed in many ways and the theoretical convergence of the algorithm is also established in the paper. The efficiency of the proposed technique was assessed by solving a set of 24 constrained problems and 15 unconstrained problems which have been proposed in IEEE-CEC sessions 2006 and 2015, respectively. The results are compared with 11 state-of-the-art algorithms for constrained problems and 6 state-of-the-art algorithms for unconstrained problems. A variety of ways are considered to examine the ability of the proposed algorithm in terms of its converging ability, success, and statistical behavior. The performance of the proposed constraint-handling method is judged by analyzing its ability to produce a feasible population. It was concluded that the proposed algorithm performs efficiently with good results as a constrained optimizer. © 2016 Elsevier B.V.
Citation: Swarm and Evolutionary Computation (2016), 31(): 64-89
URI: https://doi.org/10.1016/j.swevo.2016.07.003
http://repository.iitr.ac.in/handle/123456789/10271
Issue Date: 2016
Publisher: Elsevier B.V.
Keywords: Constrained handling
Constrained optimization
Gravitational Search Algorithm
Particle Swarm Optimization
Shrinking hypersphere
ISSN: 22106502
Author Scopus IDs: 55220521500
8561208900
35100406500
8840681600
Author Affiliations: Yadav, A., Department of Sciences and Humanities, National Institute of Technology Uttarakhand, Srinagar (Garhwal), Uttarakhand 246174, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India
Kim, J.H., School of Civil Environmental and Architectural Engineering, Korea University, Seoul, 136-713, South Korea
Nagar, A.K., Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom
Funding Details: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2013R1A2A1A01013886 ) and National Institute of Technology Uttarakhand, India . We would like to express our gratitude toward the unknown potential reviewers who have agreed to review this paper and who have provided valuable suggestions to improve the quality of the paper.
Corresponding Author: Kim, J.H.; School of Civil Environmental and Architectural Engineering, Korea UniversitySouth Korea; email: jaykim@korea.ac.kr
Appears in Collections:Journal Publications [MA]

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