Particle Swarm Optimization and Intelligence: Advances and Applications by Konstantinos E. Parsopoulos, Michael N. Vrahatis

Particle Swarm Optimization and Intelligence: Advances and Applications Konstantinos E. Parsopoulos, Michael N. Vrahatis ebook
ISBN: 1615206663, 9781615206667
Publisher: Information Science Publishing
Format: pdf
Page: 329

Evolutionary algorithms stand upon some common characteristics like stochastic, adaptive, and learning in order to produce intelligent optimization schemes. Research and Application of DNA Clustering Algorithm Based on Intelligent Algorithm. Section I – Theory and Foundation presents some of the latest developments in CI, e.g. In order to solve that the clustering algorithm based on PSO can not determine the number of clusters k, by the k-means algorithm, achieve the best number of cluster k and the structuring of the cluster validity function. Summary: Social networking tools such as Twitter and the emerging Google Wave web application are taking businesses to the frontiers of real-time communication and collaboration. Recent advances in digital imaging and computer hardware technology have led to an explosion in the use of digital images in a variety of scientific and engineering applications. Particle Swarm Optimization and Intelligence: Advances and Applications examines modern intelligent optimization algorithms proven as very efficient in applications from various scientific and technological fields. This special issue will therefore focus on recent advances in swarm intelligence applications in power engineering. In many filtering applications −3 dB frequency has become a recognizable parameter for defining the cut-off frequency (the frequency at which the magnitude attains an absolute value of 0.5). In his judgment, swarm intelligence is an evocative modeling tool that could help human beings better organize themselves and society—which could help in developing optimization algorithms and problem–solving methods. Excerpt: The Particle Swarm Optimizer (PSO), one of the pillars of Swarm Intelligence, is a remarkable algorithm for at least two reasons: (a) it has a very simple formulation which makes it easy to implement, apply, extend and hybridize, and (b) it is a constant source of complex and . Comments Off However, in most cases, the number of clusters k can not be determined in advance, so the best number of clusters k needs to be optimized. Particle swarm optimization, Web services, data mining with privacy protection, kernel methods for text analysis, etc. RGA, PSO, and DE have also been adopted for the sake of comparison. Advances of Computational Intelligence in Industrial Systems reports the exploration of CI frontiers with an emphasis on a broad spectrum of real-world applications. Section II – Industrial Application covers the CI applications in a wide variety of domains, e.g. These applications result from the interaction between fundamental Metaheuristics (evolutionary algorithms, simulated annealing, tabu search, ant colony optimization, particle swarm optimization, harmony search, bee colony optimization, etc.) Expert systems in/for.