Utilizing particle swarm optimization to label a structured beam matrix

Sun, Qin; Shi, Yuhui; Eberhart, Russell C.; Bauson, W.A.

doi:10.1109/sis.2003.1202256

Cited by 4 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a new intelligent algorithm, particle swarm optimization(PSO) algorithm was pioneered by Eberhart and Kennedy [1] .it employs global search strategy based on population,which is similar to Genetic Algorithm(GA).However,it only uses simple V-S model to finish searching optimum. A few parameters only need adjust,it owns many merits such as simplicity,more rapid convergence, better precision and so on, its dominance has been exhibited in solving of all kinds of problems [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Solving Stochastic Chance-Constrained Programming Problem

Xiao

2014

AMR

View full text Add to dashboard Cite

Stochastic chance-constrained programming which is one of important stochastic programming widely exits in different fields. For searching an algorithm that can more effectively solve this problem,a new algorithm for its combined stochastic particle swarm optimization with stochastic simulation for approximation of the fitness function and checking feasibility of solution is presented. It overcomes the defaults such as needing a long time, complex calculation,resapsing into local optimum in the hybrid intelligence algorithm based on GA. After testing its performance and comparing with GA, the results show that the algorithm is more preferable.

show abstract

Section: Introductionmentioning

confidence: 99%

An Algorithm for Solving Stochastic Chance-Constrained Programming Problem

Xiao

2014

AMR

View full text Add to dashboard Cite

show abstract

An Optimization Algorithm for Computing the Minimal Test Set of Circuits

Deng

Liu

et al. 2008

2008 International Symposium on Intelligent Information Technology Application Workshops

View full text Add to dashboard Cite

With the extraordinary development of electronic technology, complexity of circuit system increases sharply. More and more electric circuits are composed of digital and analog signal simultaneously, resulting in greater difficulty in circuit testing. Theory of Discrete Event System(DES) and relevant researches provide uniform, efficient and systematic methods for testing these mixed-signal circuits. In view of the circuit testing based on DES theory, how to find out the minimal test set of circuits is important part to shorten complicated work. Considering advantageous characteristics of the minimal test set, a novel approach involving with minimizing mixed-signal circuit testing-set algorithm is put forward, which is in light of Discrete Particle Swarm Optimization(DPSO) and can finally realize optimization. By virtue of "velocity-position" model referred to competition and cooperation among particles of swarm, an optimum solution is searched in result space. Experimental results testify high efficiency and feasibility of this proposed approach, which is superior to other optimization algorithms.

show abstract

An Evolutionary Computing Approach to Robust Design in the Presence of Uncertainties

Chan

Sudhoff

2010

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

Utilizing particle swarm optimization to label a structured beam matrix

Cited by 4 publications

References 2 publications

An Algorithm for Solving Stochastic Chance-Constrained Programming Problem

An Algorithm for Solving Stochastic Chance-Constrained Programming Problem

An Optimization Algorithm for Computing the Minimal Test Set of Circuits

An Evolutionary Computing Approach to Robust Design in the Presence of Uncertainties

Contact Info

Product

Resources

About