Computational Intelligence: Principles, Techniques and Applications

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: An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented and a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for W SNs.

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: This chapter provides two recent algorithms for evolutionary optimization – well known as particle swarm optimization (PSO) and differential evolution (DE), inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces.