Results 1 - 10 of 246

Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

by Asanga Ratnaweera, Saman K. Halgamuge, Harry C. Watson - IEEE Transactions on Evolutionary Computation , 2004
"... Abstract—This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, tim ..."
Abstract - Cited by 194 (2 self) - Add to MetaCart
, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of “mutation ” is introduced

Adaptive Particle Swarm Optimization

by Zhi-hui Zhan, Jun Zhang , 2008
"... This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an ‘evolutionary factor’ by using the population distribution information and relative ..."
Abstract - Cited by 67 (2 self) - Add to MetaCart
This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an ‘evolutionary factor’ by using the population distribution information

An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Random

by Dazhi Pan, Ying Ci, Min He, Hongying He
"... Abstract — The standard particle swarm optimization (PSO) algorithm converges very fast, while it is very easy to fall into the local extreme point. According to waiting effect among particles with mean-optimal position(MP), the quantum-behaved particle swarm optimization (QPSO) algorithm can preven ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
state is modified. In QPSO, a random weight to each particle in swarm is introduced, and according to the order of each particle’s best position fitting value, there are three evaluation programs for L(t), which are random-weight mean-optimal position(RMP), reverse-order random-weight mean-optimal

Particle Swarms for Multimodal Optimization

by Ender Özcan, Murat Yılmaz
"... Abstract. In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
Abstract. In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using

An MCMC-based Particle Filter For Tracking Multiple Interacting Targets

by Zia Khan, Tucker Balch, Frank Dellaert - in Proc. ECCV , 2003
"... We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In respon ..."
Abstract - Cited by 152 (6 self) - Add to MetaCart
We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem

Clubs-based Particle Swarm Optimization

by Wesam Elshamy, Hassan M. Emara, A. Bahgat - IEEE SWARM INTELLIGENCE SYMPOSIUM 2007 , 2007
"... This paper introduces a new dynamic neighborhood network for particle swarm optimization. In the proposed Clubs-based Particle Swarm Optimization (C-PSO) algorithm, each particle initially joins a default number of what we call ‘clubs’. Each particle is affected by its own experience and the experie ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
This paper introduces a new dynamic neighborhood network for particle swarm optimization. In the proposed Clubs-based Particle Swarm Optimization (C-PSO) algorithm, each particle initially joins a default number of what we call ‘clubs’. Each particle is affected by its own experience

Particle Filtering Optimized by Swarm Intelligence Algorithm

by Wei Jing, Hai Zhao, Chunhe Song, Dan Liu , 2009
"... A new filtering algorithm — PSO-UPF was proposed for nonlinear dynamic systems. Basing on the concept of re-sampling, particles with bigger weights should be re-sampled more time, and in the PSO-UPF, after calculating the weight of particles, some particles will join in the refining process, which m ..."
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A new filtering algorithm — PSO-UPF was proposed for nonlinear dynamic systems. Basing on the concept of re-sampling, particles with bigger weights should be re-sampled more time, and in the PSO-UPF, after calculating the weight of particles, some particles will join in the refining process, which

A New Approach to Improve Particle Swarm Optimization

by Liping Zhang, Huanjun Yu, Shangxu Hu
"... Abstract. Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniforml ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
Abstract. Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number

On Convergence and Parameter Selection of an Improved Particle Swarm Optimization

by Xin Chen, Yangmin Li - International Journal of Control, Automation, and Systems , 2008
"... Abstract: This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic app ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
Abstract: This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic

Decision Based Median Filter using Particle Swarm Optimization for Impulsive Noise

by Bharathi P. T, Dr. P. Subashini
"... Abstract: Infrared image sensors and communication medium often introduce impulse noise in image acquisition and transmission. Most commonly available filters to remove impulsive noises are median filters with different versions, but the most important drawbacks identified with them are low noise su ..."
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suppression and edge blurring. To preserve the sharp and valuable information present in the image, the filtering algorithms should preserve the information available in them. The proposed work consists of particle swarm optimization based weight adaptation approach in the design of the filter. The filter
Results 1 - 10 of 246