A novel neighborhood archives embedded gravitational constant in GSA

Due to its effective search mechanism, gravitational search algorithm (GSA) has become a 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 $$K_\mathrm{best}$$ archive and gravitational constant G. The $$K_\mathrm{best}$$ archive stores the best agents (solutions) at any evolutionary state and hence helps GSA for global search. Each agent interacts with exactly same agents of $$K_\mathrm{best}$$ archive without considering its current impact on the search process, which 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 therefore leads the swarm towards stagnation or sometimes skipping the true optima. To address these problems, an improved version of GSA called ‘A novel neighborhood archives embedded gravitational constant in GSA (NAGGSA)’ is proposed in this paper. In NAGGSA, we first propose two novel neighborhood archives for each agent which helps in increased diversified 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.