Open AccessJournal Article
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
TLDR
A parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data, which demonstrates the usefulness of the proposed PPSO algorithm.Abstract:
Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.read more
Citations
More filters
Proceedings ArticleDOI
An Adaptive Fuzzy Weight PSO Algorithm
TL;DR: In this paper, a novel adaptive fuzzy weight parameter PSO Algorithm is proposed that can regulate global search and local search, and has better search accuracy than the basic PSO and the linear decreasing inertia weight particle swarm optimization.
Journal ArticleDOI
Isolated particle swarm optimization with particle migration and global best adoption
TL;DR: Computational experience demonstrates that the designed IPSO is superior to the original version of particle swarm optimization (PSO) in terms of the accuracy and stability of the results, when isolation phenomenon, particle migration and gbest sharing are involved.
Journal ArticleDOI
A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network
TL;DR: A new heuristic algorithm named Parallel Compact Cat Swarm Optimization (PCCSO) with three separate communication strategies and the concept of the compact is presented, which is not only reflected in enhancing the ability of local search, but also in saving the computer memory.
Journal ArticleDOI
A Hybrid Improved MVO and FNN for Identifying Collected Data Failure in Cluster Heads in WSN
TL;DR: The compared result exhibits the proposed technique that provides the alternative tool with the anticipation of influence on data sets and an effective way of forwarding the correct data from CH to BS in WSN applications.
Proceedings ArticleDOI
A parallel grey wolf optimizer combined with opposition based learning
TL;DR: This research has tried to improve the final results of the original version of algorithm, compared with other common optimization approaches, using the techniques of opposition-based learning and parallelism.
References
More filters
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.
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.
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.
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.
Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.