Journal ArticleDOI
A hybrid PSO-GA algorithm for constrained optimization problems
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TLDR
Experimental results indicate that the proposed approach to solving the constrained optimization problems may yield better solutions to engineering problems than those obtained by using current algorithms.About:
This article is published in Applied Mathematics and Computation.The article was published on 2016-02-01. It has received 514 citations till now. The article focuses on the topics: Meta-optimization & Multi-swarm optimization.read more
Citations
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Journal ArticleDOI
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
TL;DR: Particle swarm optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms as discussed by the authors.
Journal ArticleDOI
A hybrid GSA-GA algorithm for constrained optimization problems
TL;DR: A new hybrid GSA-GA algorithm is presented for the constraint nonlinear optimization problems with mixed variables that is tuned up with the gravitational search algorithm and each solution is upgraded with the genetic operators such as selection, crossover, mutation.
Journal ArticleDOI
A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks
TL;DR: The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score.
Journal ArticleDOI
PSOSCALF: A new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems
TL;DR: Using combination of the SCA and Levy flight in the PSOSCALF algorithm, the exploration capability of the original PSO algorithm is enhanced and also, being trapped in the local minimum is prevented.
Journal ArticleDOI
Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application
TL;DR: A rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.