A binary variant of gravitational search algorithm and its application to windfarm layout optimization problem
This work addresses optimization challenges in binary search spaces for researchers and practitioners, with incremental improvements to the GSA framework.
The paper tackled the shortcomings of the gravitational search algorithm (GSA) in binary search spaces, such as stagnation and premature convergence, by proposing a new binary variant called BNAGGSA, which demonstrated superior performance over two existing binary GSA variants on 23 benchmark test problems and was applied to optimize windfarm layouts in two case studies.
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.