The economic load dispatch (eld) problem strives to optimize the division of total power
demand among the power generators under specified constraints. It is solved by scheduling …

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 …

Data clustering is an important technique of data mining in which the objective is to partition
N data objects into K clusters that minimize the sum of intra‐cluster distances between each …

Data clustering is a prevalent problem that belongs to the data mining domain. It aims to
partition the given data objects into some specified number of clusters based on the sum of …

Data clustering is a crucial machine learning technique that helps divide a given dataset into
many similar data objects where the data members resemble each other. It is an …

\This comprehensive reference text discusses nature inspired algorithms and their
applications. It presents the methodology to write new algorithms with the help of MATLAB …

Distinct non-random quantitative interactions at diverse timestamps formulate real-world
dynamic complex networks. The most frequently used class of methods for discovering …

Clustering plays a crucial role in data mining and machine learning, with the primary
objective being the identification of cohesive and distinct data groups, enabling the …

Environmental adaptation method (EAM) is a newly developed optimisation algorithm for
complex problems. Although EAM and its variants converge very fast in lower-dimensional …