Large-scale multi-objective optimization problems (MOPs) that involve a large number of
decision variables, have emerged from many real-world applications. While evolutionary …

Most of the optimization problems encountered in the real world are discrete type which
involves decision variables defined in the discrete search space. Binary optimization …

Artificial bee colony (ABC) algorithm has shown good performance on many optimization
problems. However, these problems mainly focus on single-objective and ordinary multi …

Image segmentation is an essential part of image analysis, which has a direct impact on the
quality of image analysis results. Thresholding is one of the simplest and widely used …

Swarm intelligence-based optimisation algorithms, inspired by the collective intelligent
behaviours of biology groups, have been widely recognised as efficient optimisers for many …

Context: Clustering algorithms, as a modularization technique, are used to modularize a
program aiming to understand large software systems as well as software refactoring. These …

Software module clustering is an unsupervised learning method used to cluster software
entities (eg, classes, modules, or files) with similar features. The obtained clusters may be …

A software system evolves over time in order to meet the needs of users. Understanding a
program is the most important step to apply new requirements. Clustering techniques …

The challenges of the most multi-objective particle swarm optimization (MOPSO) algorithms
are to improve the selection pressure, equilibrate the convergence and diversity when …

Widespread use of various mobiles, social networks and IOT devices results into continuous
generation of the data, often leading to the formation of the big data. Sustainable grouping of …