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A finite-time particle swarm optimization algorithm

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A finite-time particle swarm optimization algorithm
นักวิจัย : Lu, Qiang. , Centre for Intelligent and Networked Systems (CINS) , Han, Qing-Long. , Centre for Intelligent and Networked Systems (CINS)
คำค้น : Benchmark Functions , Finite-time Convergence , Particle Swarm Optimization Algorithm
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2555
อ้างอิง : http://hdl.cqu.edu.au/10018/932383 , acquire1-20130509-110342 , cqu:9502
ที่มา : -
ความเชี่ยวชาญ : -
ความสัมพันธ์ : -
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

This paper deals with a class of optimization problems by designing and analyzing a finite-time particle swarm optimization (FPSO) algorithm. Two versions of the FPSO algorithm, which consist of a continuous-time FPSO algorithm and a discrete-time FPSO algorithm, are proposed. Firstly, the continuous-time FPSO algorithm is derived from the continuous model of the particle swarm optimization (PSO) algorithm by introducing a nonlinear damping item that can enable the continuous-time FPSO algorithm to converge within a finite-time interval and a parameter that can enhance the exploration capability of the continuous-time FPSO algorithm. Secondly, the corresponding discrete-time version of the FPSO algorithm is proposed by employing the same discretization scheme as the generalized particle swarm optimization (GPSO) such that the exploiting capability of the discrete-time FPSO algorithm is improved. Thirdly, a Lyapunov approach is used to analyze the finite-time convergence of the continuous-time FPSO algorithm and the stability region of the discrete-time FPSO algorithm is also given. Finally, the performance capabilities of the proposed discrete-time FPSO algorithm are illustrated by using three wellknown benchmark functions (global minimum surrounded by multiple minima): Griewank, Rastrigin, and Ackley. In terms of numerical simulation results, the proposed continuous-time FPSO algorithm is used to deal with the problem of odor source localization by coordinating a group of robots.

บรรณานุกรม :
Lu, Qiang. , Centre for Intelligent and Networked Systems (CINS) , Han, Qing-Long. , Centre for Intelligent and Networked Systems (CINS) . (2555). A finite-time particle swarm optimization algorithm.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Lu, Qiang. , Centre for Intelligent and Networked Systems (CINS) , Han, Qing-Long. , Centre for Intelligent and Networked Systems (CINS) . 2555. "A finite-time particle swarm optimization algorithm".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Lu, Qiang. , Centre for Intelligent and Networked Systems (CINS) , Han, Qing-Long. , Centre for Intelligent and Networked Systems (CINS) . "A finite-time particle swarm optimization algorithm."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2555. Print.
Lu, Qiang. , Centre for Intelligent and Networked Systems (CINS) , Han, Qing-Long. , Centre for Intelligent and Networked Systems (CINS) . A finite-time particle swarm optimization algorithm. กรุงเทพมหานคร : Central Queensland University, Australia; 2555.