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.

中文翻译：

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引力搜索算法的二值变体及其在风电场布局优化问题中的应用

在二分搜索空间中，GSA 框架遇到了停滞、多样性损失、早熟收敛和时间复杂度高等缺点。为了解决这些问题，本文提出了一种新的 GSA 二进制变体，称为“一种在 GSA 中嵌入引力常数的新邻域档案，用于二进制搜索空间 (BNAGGSA)”。在 BNAGGSA 中，新的基于适应度距离的社交互动策略产生了一种自适应步长机制，通过该机制，代理根据其当前的搜索要求以最佳步长向最佳方向移动。在 23 个众所周知的基准测试问题上，将所提出算法的性能与 GSA 的两个二进制变体进行了比较。实验结果和统计分析证明了 BNAGGSA 优于比较算法。此外，为了检查所提出算法在解决实际应用中的适用性，考虑了风电场布局优化问题。实验中考虑了具有两个不同风场的两个不同风数据集的两个案例研究。