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
A Novel Bat Algorithm based on Collaborative and Dynamic Learning of Opposite Population
TL;DR: A novel bat algorithm based on collaborative and dynamic learning of opposite population is proposed that adapts a collaborative strategy to generate the opposite population and more possible opposite individuals can be dynamically learned and added to the population.
Journal ArticleDOI
Collaborative Energy Management Optimization Toward a Green Energy Local Area Network
TL;DR: An energy management optimization model is proposed that addresses ELAN operations and includes pollution treatment fees and fulfills the optimal allocation of energy and that the PHEV intelligent charging/discharging strategy promotes economic benefits for the network.
Journal ArticleDOI
A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems.
Shuang Wang,Qingxin Liu,Yuxiang Liu,Heming Jia,Laith Abualigah,Laith Abualigah,Rong Zheng,Di Wu +7 more
TL;DR: In this article, a hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems, where SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms' advantages to enhance global optimization performance.
Journal ArticleDOI
Opposition-based learning for competitive hub location: A bi-objective biogeography-based optimization algorithm
TL;DR: To enhance the performance of the proposed Pareto-based algorithms, this paper intends to develop a binary opposition-based learning as a diversity mechanism for both algorithms.
Journal ArticleDOI
Sine Cosine Algorithm with Multigroup and Multistrategy for Solving CVRP
TL;DR: Numerical experimental results show that the performance of the MMSCA algorithm is better than that of the original SCA algorithm, and it also has some advantages over other intelligent algorithms.
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.