  In the binary search space, GSA framework encounters the shortcomings of
stagnation, diversity loss, premature convergence and high time complexity. To
address these issues, a novel binary variant of GSA called `A novel
neighbourhood archives embedded gravitational constant in GSA for binary search
space (BNAGGSA)' is proposed in this paper. In BNAGGSA, the novel
fitness-distance based social interaction strategy produces a self-adaptive
step size mechanism through which the agent moves towards the optimal direction
with the optimal step size, as per its current search requirement. The
performance of the proposed algorithm is compared with the two binary variants
of GSA over 23 well-known benchmark test problems. The experimental results and
statistical analyses prove the supremacy of BNAGGSA over the compared
algorithms. Furthermore, to check the applicability of the proposed algorithm
in solving real-world applications, a windfarm layout optimization problem is
considered. Two case studies with two different wind data sets of two different
wind sites is considered for experiments.
