Hybrid Binary Grey Wolf With Harris Hawks Optimizer for Feature Selection
Hybrid Binary Grey Wolf With Harris Hawks Optimizer for Feature Selection
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DOI：
10.1109/access.2021.3060096
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Talpur, Noureen
中科院分区：
文献类型：
--
作者：
Al-Wajih, Ranya;Abdulkadir, Said Jadid;Talpur, Noureen
尽管灰狼优化器 (GWO) 在许多领域表现出色，但局部最优区域的停滞可能仍然是一个问题。可以探索几个重要的 GWO 因素来增强分类选择的性能，在使用或建模元启发式方法、探索搜索领域和开发最佳解决方案时要考虑两个相互冲突的概念。以良好的方式平衡探索和利用将提高搜索算法的性能。为了实现良好的平衡，本文提出了一种二元混合 GWO 和 Harris Hawks Optimization (HHO)，形成一种称为 HBGWOHHO 的模因方法。使用sigmoid传递函数将连续搜索空间转换为二元搜索空间，以满足特征选择性质的要求。基于包装器的 k-近邻用于评估所选特征的优劣性。为了验证所提出方法的性能，使用了 18 个标准 UCI 基准数据集。所提出的混合方法的性能与二元灰狼优化器（BGWO）、二元粒子群优化器（BPSO）、二元Harris Hawks优化器（BHHO）、二元遗传算法（BGA）和二元混合BWOPSO进行了比较。研究结果表明，所提出的方法有效地提高了 BGWO 算法的性能。所提出的混合方法在精度、所选特征大小和计算时间方面优于 BGWO 算法。类似地，与 BPSO 和 BGA 特征选择算法相比，所提出的 HBGWOHHO 超越了它们，产生了更好的精度，在更短的计算时间内选择的特征尺寸更小。
Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in local optima areas may still be a concern. Several significant GWO factors can be explored to enhance the performance of selection in classification, with two conflicting concepts to be considered in using or modeling a metaheuristic method, exploring a search field, and exploiting optimal solutions. Balancing exploration and exploitation in a good manner will improve the search algorithm's performance. To achieve a good balance, this paper proposes a binary hybrid GWO and Harris Hawks Optimization (HHO) to form a memetic approach called HBGWOHHO. The sigmoid transfer function is used to transfer the continuous search space into a binary one to meet the feature selection nature requirement. A wrapper-based k-Nearest neighbor is used to evaluate the goodness of the selected features. To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. The findings revealed that the proposed method was effective in improving the performance of the BGWO algorithm. The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. Similarly, compared with BPSO and BGA feature selection algorithms, the proposed HBGWOHHO surpassed them yield better accuracy, the smaller size of selected features in much lower computational time.