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246
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
- 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 ..."
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Cited by 194 (2 self)
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, 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
, 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 ..."
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Cited by 67 (2 self)
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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
"... 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 ..."
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Cited by 1 (0 self)
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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
"... 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 ..."
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Cited by 6 (0 self)
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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
- 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 ..."
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Cited by 152 (6 self)
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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
- 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 ..."
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Cited by 3 (1 self)
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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
, 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
"... 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 ..."
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Cited by 5 (0 self)
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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
- 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 ..."
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Cited by 3 (1 self)
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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
"... 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
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246