Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India.
Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India.
Sensors (Basel). 2021 Jun 14;21(12):4086. doi: 10.3390/s21124086.
A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.
一种 k-均值算法是一种聚类方法，已经得到了广泛的认可。然而，它的性能极大地依赖于聚类中心的初始值。此外，由于其探索能力较弱，很容易陷入局部最优解。最近，一种名为 moth flame optimizer（MFO）的新启发式算法被提出，用于处理复杂问题。MFO 模拟了飞蛾的智能，即横向定位，用于在自然界中导航。在各种研究工作中，MFO 的性能被发现相当令人满意。本文提出了一种基于 MFO 的新启发式方法来解决数据聚类问题。为了验证所提出方法的竞争力，使用了 Shape 和 UCI 基准数据集进行了各种实验。将所提出的方法与五种最先进的算法在 12 个数据集上进行了比较。在所提出的算法的平均性能在 10 个数据集上是优越的，而在剩下的两个数据集上是可比的。实验结果的分析证实了所提出方法的有效性。
