From the past few decades many nature inspired algorithms have been developed and
gaining more popularity because of their effectiveness in solving problems of distinct …

The partitional clustering concept started with K-means algorithm which was published in
1957. Since then many classical partitional clustering algorithms have been reported based …

In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …

This paper proposes a novel nature-inspired optimization algorithm called the Fox optimizer
(FOX) which mimics the foraging behavior of foxes in nature when hunting preys. The …

Data clustering involves identifying latent similarities within a dataset and organizing them
into clusters or groups. The outcomes of various clustering algorithms differ as they are …

Machine learning (ML) has a well-established reputation for successfully enabling
automation through its scalable predictive power. Industry 4.0 encapsulates a new stage of …

Effective feedback can reduce building power consumption and carbon emissions.
Therefore, providing information to building managers and tenants is the first step in …

Power system applications often require solving one or multiple optimization problems
which are nonlinear. Due to the limitations such as dimensionality constraints and slow …

As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …

The spectral clustering algorithm is extensively employed in different aspects, especially in
the field of pattern recognition. However, the efficient construction of the neighborhood …