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Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.


Papers
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Proceedings ArticleDOI
19 Dec 2011
TL;DR: Experimental results obtained in a simulated environment show that biological and sociological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue).
Abstract: This paper proposes two extensions of Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO), respectively named as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biological-inspired techniques to the domain of multi-robot systems, by taking into account obstacle avoidance. These novel algorithms are demonstrated for groups of simulated robots performing a distributed exploration task. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a “punish-reward” mechanism enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment show that biological and sociological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue).

143 citations

Journal ArticleDOI
TL;DR: This is the first attempt to develop a PSO hyper-heuristic and apply to the classic RCPSP and the promising computational results validate the effectiveness of the proposed approach.

142 citations

Dissertation
22 Feb 2006
TL;DR: This thesis investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing and proposes three approaches to tackle the color image quantization and spectral unmixing problems.
Abstract: Pattern recognition has as its objective to classify objects into different categories and classes. It is a fundamental component of artificial intelligence and computer vision. This thesis investigates the application of an efficient optimization method, known as Particle Swarm Optimization (PSO), to the field of pattern recognition and image processing. First a clustering method that is based on PSO is proposed. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. A new automatic image generation tool tailored specifically for the verification and comparison of various unsupervised image classification algorithms is then developed. A dynamic clustering algorithm which automatically determines the "optimum" number of clusters and simultaneously clusters the data set with minimal user interference is then developed. Finally, PSO-based approaches are proposed to tackle the color image quantization and spectral unmixing problems. In all the proposed approaches, the influence of PSO parameters on the performance of the proposed algorithms is evaluated. Key terms: Clustering, Color Image Quantization, Dynamic Clustering, Image Processing, Image Segmentation, Optimization Methods, Particle Swarm Optimization, Pattern Recognition, Spectral Unmixing, Unsupervised Image Classification. Thesis supervisor: Prof. A. P. Engelbrecht Thesis co-supervisor: Dr. Ayed Salman Department of Computer Engineering, Kuwait University, Kuwait Department of Computer Science Degree: Philosophiae Doctor University of Pretoria etd – Omran, M G H (2005)

142 citations

Journal ArticleDOI
TL;DR: A novel hybrid Krill herd and quantum-behaved particle swarm optimization, called KH–QPSO, is presented for benchmark and engineering optimization and can easily infer that it is more efficient than other optimization methods for solving standard test problems andengineering optimization problems.
Abstract: A novel hybrid Krill herd (KH) and quantum-behaved particle swarm optimization (QPSO), called KH---QPSO, is presented for benchmark and engineering optimization QPSO is intended for enhancing the ability of the local search and increasing the individual diversity in the population KH---QPSO is capable of avoiding the premature convergence and eventually finding the function minimum; especially, KH---QPSO can make all the individuals proceed to the true global optimum without introducing additional operators to the basic KH and QPSO algorithms To verify its performance, various experiments are carried out on an array of test problems as well as an engineering case Based on the results, we can easily infer that the hybrid KH---QPSO is more efficient than other optimization methods for solving standard test problems and engineering optimization problems

142 citations

Book ChapterDOI
01 Jan 2008

142 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023183
2022471
202110
20207
201926
2018171