In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics
have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve
using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization
and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of
test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers,
students and practitioners working in the fields of artificial intelligence, optimization and data analytics.