Abstract
Gravitational search algorithm is a newly born metaheuristic which is inspired by the working force of attraction between two masses. In this article gravitational search algorithm is employed to solve the constrained optimization problems. A variety of state-of-the-art benchmark problems are taken into account to justify the performance of the gravitational search algorithm. The results of gravitational search algorithms are compared with two state-of-the-art Particle Swarm Optimization algorithms in various aspects. Pairwise one tailed t test is applied to justify the statistical significance of the results and time complexity analysis is also performed. Finally the conclusions are drawn based on the experimental results.
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Acknowledgments
The first author is thankful to Council of Scientific and Industrial Research, HRDG Group for the financial support with grant No. 9834-11-44. I would also like to thanks to the anonymous reviewers for their valuable suggestion and comments for revising this article.
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Yadav, A., Deep, K. Constrained Optimization Using Gravitational Search Algorithm. Natl. Acad. Sci. Lett. 36, 527–534 (2013). https://doi.org/10.1007/s40009-013-0165-8
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DOI: https://doi.org/10.1007/s40009-013-0165-8