Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

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 cluster analysis, a fundamental problem is to determine the best estimate of the number of
clusters; this is known as the automatic clustering problem. Because of lack of prior domain …

This paper presents an efficient and reliable evolutionary-based approach to solve the
Optimal Power Flow (OPF) problem by considering the emission issue. The OPF problem …

One of the most effective approaches to increase the lifetime of wireless sensor networks
(WSNs), is the use of a mobile sink to collect data from sensor. In WSNs, mobile sinks …

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm
intended to improve the optimization capabilities of the conventional grey wolf optimizer in …

This chapter presents the details of existing optimization algorithms such as Genetic
Algorithm (GA), Artificial Immune Algorithm (AIA), Differential Evolution (DE), Biogeography …

The on-demand provisioning and resource availability in cloud computing make it ideal for
executing scientific workflow applications. An application can start execution with a minimum …

Distributed computing paradigms such as cloud, mobile, Internet of Things, and Fog have
enabled new modalities for building enterprise architectures through service composition …

Nature-inspired algorithms have gained immense popularity in recent years to tackle hard
real world (NP hard and NP complete) problems and solve complex optimization functions …