Swagatam Das

TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.

TL;DR: It is found that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research on DE.

TL;DR: A family of improved variants of the DE/target-to-best/1/bin scheme, which utilizes the concept of the neighborhood of each population member, and is shown to be statistically significantly better than or at least comparable to several existing DE variants as well as a few other significant evolutionary computing techniques over a test suite of 24 benchmark functions.

TL;DR: Differential evolution has emerged as one of the fast, robust, and efficient global search heuristics of current interest as mentioned in this paper, which has been applied to the automatic clustering of large unlabeled data sets.

TL;DR: A new mutation strategy, a fitness- induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance are proposed.