DocumentCode :
2838102
Title :
A Quantum-Inspired Ant Colony Optimization for robot coalition formation
Author :
Yu, Zhang ; Shuhua, Liu ; Shuai, Fu ; Di, Wu
Author_Institution :
Sch. of Comput. Sci., Northeast Normal Univ., Changchun, China
fYear :
2009
fDate :
17-19 June 2009
Firstpage :
626
Lastpage :
631
Abstract :
A quantum-inspired ant colony optimization (QACO), based on the concept and principles of quantum computing is proposed in this paper to improve the ability to search and optimization of ant colony optimization (ACO). Each ant is a quantum individual and instead of Q-bit code, we use the probability of choosing robots, and QACO is successfully applied to solve robot coalition formation. The simulated results show that QACO has the better diversity of population and ability to search and optimization, and performs well, even with a small population, without premature convergence as compared to ACO.
Keywords :
combinatorial mathematics; multi-robot systems; optimisation; probability; quantum computing; search problems; Q-bit code; QACO; combinatorial optimization problem; multirobot system; premature convergence; probability; quantum computing; quantum-inspired ant colony optimization; robot coalition formation; search problem; Ant colony optimization; Cameras; Computational modeling; Computer science; Convergence; Mobile robots; Quantum computing; Quantum mechanics; Robot sensing systems; Robot vision systems; Large-scale robots; QACO; Robot Coalition Formation; Task Allocation; task allocation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
Type :
conf
DOI :
10.1109/CCDC.2009.5194884
Filename :
5194884
Link To Document :
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