Metaheuristics for Data Clustering and Image Segmentation

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.

Presents recent research on metaheuristics for data clustering and image segmentation Includes a detailed study of the differential evolution and flower pollination algorithms Written primarily for academic researchers who are interested in metaheuristics, data mining, computational intelligence, and intelligent data management

Inhalt
Introduction.- METAHEURISTICS AND DATA CLUSTERING.- REVISED MUTATION STRATEGY FOR DIFFERENTIAL EVOLUTION ALGORITHM.- SEARCH strategy Flower Pollination Algorithm with Differential Evolution.