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.
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Yadav, A., Deep, K. (2014). A Novel Co-swarm Gravitational Search Algorithm for Constrained Optimization. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1771-8_55
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DOI: https://doi.org/10.1007/978-81-322-1771-8_55
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