Optimal PSO for collective robotic search applications

被引：104

作者：

Doctor, S ^{[1
]}

Venayagamoorthy, GK ^{[1
]}

Gudise, VG ^{[1
]}

机构：

[1] Univ Missouri, Dept Elect & Comp Engn, Rolla, MO 65409 USA

来源：

CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 | 2004年

关键词：

D O I：

10.1109/CEC.2004.1331059

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This paper presents the application of Particle Swarm Optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches.

引用

页码：1390 / 1395

页数：6

共 9 条

[1] Event-based motion control for mobile-sensor networks [J].

Butler, Z ;

Rus, D .

IEEE PERVASIVE COMPUTING, 2003, 2 (04) :34-42

[2]

Doctor S, 2004, PROCEEDINGS OF INTERNATIONAL CONFERENCE ON INTELLIGENT SENSING AND INFORMATION PROCESSING, P249

[3]

Eberhart RC, 2001, IEEE C EVOL COMPUTAT, P81, DOI 10.1109/CEC.2001.934374

[4] Mobile sensor-actuator networks: Opportunities and challenges [J].

Haenggi, M .

CELLULAR NEURAL NETWORKS AND THEIR APPLICATIONS, 2002, :283-290

[5]

Kenndy J., 1995, P IEEE INT C NEUR NE, V4, P1942, DOI [10.4018/ijmfmp.2015010104, DOI 10.4018/IJMFMP.2015010104]

[6]

Kennedy J., 2001, SWARM INTELLIGENCE

[7]

Shi Y., 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), P1945, DOI 10.1109/CEC.1999.785511

[8]

Shi Y., 1998, P 7 ANN C EV PROGR, V1447, P591, DOI [DOI 10.1007/BFB0040810, 10.1007/BFb0040810]

[9] A modified particle swarm optimizer [J].

Shi, YH ;

Eberhart, R .

1998 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION - PROCEEDINGS, 1998, :69-73

← 1 →