Distance based multiple swarms formulation method in particle swarm optimization

2016 ◽  
Author(s):  
Junpei Tsuji ◽  
Masato Noto
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multiple Swarms

scholarly journals Video Superresolution via Parameter-Optimized Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yunyi Yan ◽  
Yujie He ◽  
Yingying Hu ◽  
Baolong Guo
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Video Sequence ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Objective Standard ◽  
Objective Performance ◽  
Multiple Swarms

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality.

DIVERSITY-BASED INFORMATION EXCHANGE AMONG MULTIPLE SWARMS IN PARTICLE SWARM OPTIMIZATION

2008 ◽  
Vol 07 (01) ◽  
pp. 57-75 ◽  
Author(s):  
GARY G. YEN ◽  
MOAYED DANESHYARI
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Particle Swarm ◽  
Evolutionary Search ◽  
Test Functions ◽  
Swarm Optimization ◽  
Local Optima ◽  
Common Interests ◽  
Multiple Swarms ◽  
Benchmark Test Functions

This paper proposes a method to exchange information among multiple swarms in particle swarm optimization (PSO) to facilitate evolutionary search. The algorithm is developed to solve problems having landscapes with a large number of local optima. Each swarm maintains two sets of particles; one set includes the particles to be shared with other swarms, while the other involves the particles to be replaced by individuals from other swarms. The proposed algorithm also provides a new design to search for neighboring swarms in order to share common interests among the swarm's neighborhood. The particle's movement is according to one variation of PSO with three basic terms, each one to lead the particles toward the best particle in the swarm, in the neighborhood, and in the whole population. Demonstrated through a suite of benchmark test functions, the proposed algorithm shows competitive performance with improved convergence speed.

A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization

2011 ◽  
Vol 225-226 ◽  
pp. 619-622
Author(s):  
Xiang Jun Yang ◽  
Yi Long Zhao ◽  
Yu Chuang Chen ◽  
Xin Chao Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Information Sharing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Single Population ◽  
Swarm Optimization ◽  
Convergence And Stability ◽  
Multiple Swarms

Combined with a variety of ideas a Multi-swarm cooperative Perturbed Particle Swarm Optimization algorithm (MpPSO) is presented to improve the performance and to reduce the premature convergence of PSO. This algorithm includes the idea of multiple swarms to improve the evolution efficiency by information sharing between populations to avoid falling into local optimum caused by single population. It also includes the idea of perturbing the swarms beside the global best solution, which can escape from local optimum. Experiments show that the proposed algorithm MpPSO has better performance, better convergence and stability when comparing with the traditional and the recently improved particle swarm optimization.

A Novel Neural Fuzzy Network Using a Hybrid Evolutionary Learning Algorithm

2010 ◽  
pp. 250-273
Author(s):  
Cheng-Jian Lin ◽  
Cheng-Hung Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Evolutionary Learning ◽  
Functional Link ◽  
Swarm Optimization ◽  
Multiple Swarms ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy

This chapter presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This chapter uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed functional-link-based neural fuzzy network with cultural cooperative particle swarm optimization (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.

Classification of Facial Emotions using Guided Particle Swarm Optimization I

2010 ◽  
pp. 36-46
Author(s):  
Bashir Mohammed Ghandi ◽  
Ramachandran Nagarajan ◽  
Hazry Desa
Keyword(s):  
Particle Swarm Optimization ◽  
Video Clip ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Novel Approach ◽  
Specific Emotion ◽  
The Face ◽  
Multiple Swarms

This paper presents a novel approach to facial emotion detection using a modified Particle Swarm Optimization algorithm, which we called Guided Particle Swarm Optimization (GPSO). The approach involves tracking the movements of 10 Action Units (AUs) placed at appropriate points on the face of a subject and captured in video clips. Two dimensional rectangular domains are defined around each of the AUs and Particles are then defined to have a component in each domain, effectively creating a 10- dimensional search space within which particles fly in search of a solution. Since there are more than one possible target emotions at any point in time, multiple swarms are used, with each swarm having a specific emotion as its target. At each frame in the video clip, the solution of the swarm that is nearest to its target is accepted as the solution. Our results so far show the approach to have a promising success rate.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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