A binary variant of gravitational search algorithm and its application to windfarm layout optimization problem

TL;DR

This paper introduces BNAGGSA, a novel binary gravitational search algorithm variant that enhances search efficiency and effectiveness, demonstrated through benchmark tests and real-world windfarm layout optimization case studies.

Contribution

The paper proposes BNAGGSA, a new binary GSA variant with a self-adaptive step size mechanism, improving performance over existing binary GSA algorithms.

Findings

BNAGGSA outperforms existing binary GSA variants on benchmark problems.

BNAGGSA effectively solves windfarm layout optimization problems.

Statistical analysis confirms the superiority of BNAGGSA.

Abstract

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.