Abstract

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.

Highlights

  • 45, Kit Yan Chan, Sai Ho Ling, Hung Nguyen and Frank Jiang, A hypoglycemic episode diagnosis system based on neural networks for Type 1 diabetes mellitus

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Summary

Introduction

45, Kit Yan Chan, Sai Ho Ling, Hung Nguyen and Frank Jiang, A hypoglycemic episode diagnosis system based on neural networks for Type 1 diabetes mellitus. 66, Long Wen, Liang Gao, Xinyu Li, Guohui Zhang and Yang Yang, Application of Free Pattern Search on the Surface Roughness Prediction in End Milling 93, Yao Liu, Yuk Ying Chung and Wei Chang Yeh, Simplified Swarm Optimization with Sorted Local Search for Golf Data Classification 104, Qiang Lu and Qing-Long Han, A Finite-time Particle Swarm Optimization Algorithm

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