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Title: A novel co-swarm gravitational search algorithm for constrained optimization
Authors: Yadav A.
Deep K.
Bansal J.C.
Pant M.
Deep K.
Nagar A.
Published in: Proceedings of Advances in Intelligent Systems and Computing
Abstract: In this article a new co-swarm Gravitational Search Algorithm is proposed to solve the non-linear constrained optimization problems. The idea of Gravitational search algorithm (GSA) and Differential Evolution (DE) is inherited to proposed a new robust search algorithm. The individual influences of GSA and DE over the particles is incorporated collectively to provide a more effective influence in comparison to the individual influences of the GSA and DE. A new velocity update equation is propose to update the positions of the particles. To evaluate the availability of the proposed algorithm a state-of-the-art problems proposed in IEEE CEC 2006 is solved and the results are compared with GSA and DE. The supremacy of the proposed algorithm is benchmarked over the exhaustive simulation results, feasibility rate and success rate. © Springer India 2014.
Citation: Proceedings of Advances in Intelligent Systems and Computing, (2014), 629- 640
Issue Date: 2014
Publisher: Springer Verlag
Keywords: Crossover
Differential evolution
Gravitational search algorithm
Constrained optimization
Evolutionary algorithms
Learning algorithms
Soft computing
Constrained optimi-zation problems
Differential Evolution
Gravitational search algorithm (GSA)
Gravitational search algorithms
Robust search algorithms
Velocity update equation
Problem solving
ISBN: 9790000000000
ISSN: 21945357
Author Scopus IDs: 55220521500
Author Affiliations: Yadav, A., Department of Applied Sciences, ITM University, Gurgaon, India
Deep, K., Department of Mathematics, Indian Institute of Technology, Roorkee, India
Corresponding Author: Yadav, A.; Department of Applied Sciences, ITM UniversityIndia; email:
Appears in Collections:Conference Publications [MA]

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