Journal ArticleDOI
Enhancing particle swarm optimization using generalized opposition-based learning
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TLDR
An enhanced PSO algorithm called GOPSO is presented, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome the problem of premature convergence when solving complex problems.About:
This article is published in Information Sciences.The article was published on 2011-10-01. It has received 384 citations till now. The article focuses on the topics: Multi-swarm optimization & Particle swarm optimization.read more
Citations
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Proceedings ArticleDOI
Opposition-based adaptive differential evolution
Xin Zhang,Shiu Yin Yuen +1 more
TL;DR: The proposed approach is called opposition-based adaptive DE, called OADE, which uses two pools to respectively store parameters and opposite parameters and significantly outperforms the benchmark algorithms.
Proceedings ArticleDOI
Particle swarm optimization with generalized opposition based learning in particle's pbest position
TL;DR: An improved Particle Swarm Optimizer with opposition based learning method employed in personal best position of particles called pbest position in order to improve the performance of particle swarm optimizer is presented.
Journal ArticleDOI
Morphing Wing Structural Optimization Using Opposite-Based Population-Based Incremental Learning and Multigrid Ground Elements
Suwin Sleesongsom,Sujin Bureerat +1 more
TL;DR: The results show that using OMPBIL in combination with a multigrid design approach is the best design strategy and the former is superior to MPBIL since the former provides better population diversity.
Journal ArticleDOI
MRI brain lesion segmentation using generalized opposition-based glowworm swarm optimization
TL;DR: An improved glowworm swarm optimization algorithm with generalized opposition-based learning is proposed in this paper and is used in segmentation for magnetic resonance images and statistically outperforms other methodologies.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
An Introduction to Probability Theory and Its Applications
David A. Freedman,William Feller +1 more
Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.