Title :
Glowworm swarm optimization algorithm with Quantum-behaved properties
Author :
Jiangshao Gu ; Kunmei Wen
Author_Institution :
Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
Since the original Glowworm Swarm Optimization (GSO) contains the defects of being caught in local optima potentially and slow convergence rate, we analyze the superiority of quantum system and introduce this technique into the behavior of glowworms according to local conditions, to propose Quantum-behaved Glowworm Swarm Optimization (QGSO). By a series of improvements, the diversity of swarms is enhanced and the oscillation caused by constant step length is eliminated. Large experiments were conducted and it is illustrated that QGSO performs consistently to keep a better balance between exploration and exploitation, and evolves faster compared with the existing competitors.
Keywords :
convergence; optimisation; quantum computing; QGSO algorithm; constant step length; convergence rate; exploitation capability; exploration capability; glowworm behavior; local conditions; local optima; oscillation elimination; quantum system; quantum-behaved glowworm swarm optimization algorithm; quantum-behaved properties; swarm diversity enhancement; Benchmark testing; Convergence; Equations; Mathematical model; Optimization; Particle swarm optimization; Wave functions; glowworm swarm optimization; meta-heuristics; optimization problems; quantum-behaved;
Conference_Titel :
Natural Computation (ICNC), 2014 10th International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4799-5150-5
DOI :
10.1109/ICNC.2014.6975874