Journal ArticleDOI
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Nasir,Swagatam Das,Dipankar Maity,Soumyadip Sengupta,Udit Halder,Ponnuthurai Nagaratnam Suganthan +5 more
TLDR
This paper presents a variant of single-objective PSO called Dynamic Neighborhood Learning Particle Swarm Optimizer (DNLPSO), which uses learning strategy whereby all other particles' historical best information is used to update a particle's velocity as in CLPSO.About:
This article is published in Information Sciences.The article was published on 2012-11-01. It has received 182 citations till now. The article focuses on the topics: Multi-swarm optimization & Particle swarm optimization.read more
Citations
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Journal ArticleDOI
Book review: particle swarm optimization for single objective continuous space problems: A review
TL;DR: This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm and presents some potential areas for future study.
Journal ArticleDOI
Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions
TL;DR: An adaptive LS starting strategy is proposed by utilizing the proposed quasi-entropy index to address its key issue, i.e., when to start LS.
Journal ArticleDOI
Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems
TL;DR: The competitive and cooperative PSO with ISM (CCPSO-ISM) is capable to prevent the premature convergence when solving global optimization problems and is validated under different test environments such as biased initialization, coordinate rotated and high dimensionality.
Journal ArticleDOI
Particle swarm optimization based on dimensional learning strategy
Guiping Xu,Quanlong Cui,Xiaohu Shi,Hongwei Ge,Zhi-Hui Zhan,Heow Pueh Lee,Yanchun Liang,Ran Tai,Chunguo Wu +8 more
TL;DR: A dimensional learning strategy (DLS) for discovering and integrating the promising information of the population best solution according to the personal best experience of each particle is proposed.
Journal ArticleDOI
Galactic Swarm Optimization
TL;DR: Extensive simulation results show that the GSO algorithm proposed in this paper converges faster to a significantly more accurate solution on a wide variety of high dimensional and multimodal benchmark optimization problems.
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.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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.
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.
Book ChapterDOI
Individual Comparisons by Ranking Methods
TL;DR: The comparison of two treatments generally falls into one of the following two categories: (a) a number of replications for each of the two treatments, which are unpaired, or (b) we may have a series of paired comparisons, some of which may be positive and some negative as mentioned in this paper.