A novel neighbourhood archives embedded gravitational constant in GSA

Joshi, Susheel Kumar and Gopal, Anshul and Singh, Shitu and Nagar, Atulya K. and Bansal, Jagdish Chand (2021) A novel neighbourhood archives embedded gravitational constant in GSA. Soft Computing. ISSN 1433-7479 (Accepted for Publication)

Text NAGGSA.pdf - Accepted Version Download (8MB)

Abstract

Due to its e�ective search mechanism, gravitational search algorithm (GSA) has became very popular and robust tool for the global optimization in a very short span of time. The search mechanism of GSA is based on its two features, namely Kbest archive and gravitational constant G. The Kbest archive stores the best agents (solutions) at any evolutionary state and hence helps GSA in search globally. Each agent interacts with exactly same agents of Kbest archive without considering its current impact on the search process, results, a rapid loss of diversity, premature convergence and the high time complexity in GSA model. On the other hand, the exponentially decreasing behavior of G scales the step size of the agent. However, this scaling is same for all agents which may cause inappropriate step size for their next move, and thus leads the swarm towards stagnation or sometimes skipping the true optima. To address these problems, an improved version of GSA called `A novel neighbourhood archives embedded gravitational constant in GSA (NAGGSA)' is proposed in this paper. In NAGGSA, we �first propose two novel neighbourhood archives for each agent which helps in increased diversi�fied search with less time complexity. Secondly, a novel gravitational constant is proposed for each agent according to the distance-fitness based scaling mechanism. The performance of the proposed variant is tested over different suites of well-known benchmark test functions. Experimental results and statistical analyses reveal that NAGGSA remarkably outperforms the compared algorithms.

Item Type:	Article
Additional Information and Comments:	This article has been accepted for publication in Soft Computing. The final published version will be available from https://www.springer.com/journal/500
Keywords:	Neighbourhood archive, Gravitational Search Algorithm (GSA), Gravitational, Constant, Meta-heuristics, Swarm Intelligence, Nature inspired optimization.
Faculty / Department:	Faculty of Human and Digital Sciences > Mathematics and Computer Science
Depositing User:	Atulya Nagar
Date Deposited:	12 Feb 2021 12:01
Last Modified:	12 Feb 2021 12:01
URI:	https://hira.hope.ac.uk/id/eprint/3241

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