Automatic clustering based hybrid metaheuristic algorithms has attracted the center of
interest of scientists and engineers which become a hot topic for different data analysis …

Data clustering is among the commonly investigated types of unsupervised learning; owing
to its ability for capturing the underlying information. Accordingly, data clustering has an …

As a novel hybrid optimization approach, the single and multi-objective BAHPSO are
investigated for thermal designing of the cross-flow plate fin heat exchanger (PFHE) under …

In this Chapter, the initial Sects. 2.1–2.4 give detailed analysis on types of metaheuristics
namely Genetic Algorithm, Particle Swarm Optimization, Differential Evolution algorithm and …

Differential Evolution (DE) is a method of optimization used in symmetrical optimization
problems and also in problems that are not even continuous, and are noisy and change over …

This paper proposes an improved variant of the harmony search (HS) algorithm called
HSGS for solving clustering problems. The HSGS generates two new harmonies at each …

One of the most interesting and promising approaches to the analysis of multivariate
phenomena and processes are methods of cluster analysis or automatic classification of …

Performance modelling of a deep learning application is essential to improve and quantify
the efficiency of the model framework. However, existing performance models are mostly …

In data, analysis clustering plays a major role. In the past decade varieties of clustering
algorithms are proposed and produced better results. But many of them required prior …

Differential search (DSA) is a recently proposed evolutionary algorithm mimicking the
Brownian motion-like random movement existing in living beings. Though it has displayed …