Title :
An improved quantum-behaved particle swarm optimization algorithm
Author :
Yang, Jie ; Xie, Jiahua
Author_Institution :
Sch. of Comput. & Inf., Shanghai Second Polytech. Univ., Shanghai, China
Abstract :
Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithm, which shows good search ability in many optimization problems. In this paper, we present an improved QPSO algorithm, called IQPSO, by combining QPSO and an opposition-based learning concept. Experimental studies on four well-known benchmark problems show that IQPSO achieves better results than QPSO and other variants of PSO on majority of test problems.
Keywords :
evolutionary computation; learning (artificial intelligence); particle swarm optimisation; IQPSO; convergence guaranteed algorithm; evolutionary computation; opposition-based learning concept; quantum-behaved particle swarm optimization algorithm; Asia; Automatic control; Benchmark testing; Convergence; Informatics; Particle swarm optimization; Quantum computing; Robot control; Robotics and automation; Sun; evolutionary computation; optimization; particle swarm optimization (PSO); quantum;
Conference_Titel :
Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5192-0
Electronic_ISBN :
1948-3414
DOI :
10.1109/CAR.2010.5456744