Authors: Hongyuan Gao; Chenwan Li
Addresses: College of Information and Communication Engineering, Harbin Engineering University, Harbin, China ' College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Abstract: A novel intelligence algorithm for continuous optimisation problem is proposed in this paper, termed as opposition-based quantum firework algorithm (OQFA). The proposed OQFA combines fireworks algorithm (FA) and two improved operators: opposition-based learning and quantum computing theory. The opposition-based learning operator can accelerate the convergence rate of algorithm by retaining the better solution, and the quantum computing theory can ameliorate the capability of searching and enhance the exploration efficiency of the solution space. Since OQFA has the features of both opposition-based learning and quantum computing, it has a high possibility to find a global optimum and avoids premature convergence. Experimental results on five test functions show that OQFA outperforms cultural algorithm (CA), particle swarm optimisation (PSO) and FA in terms of convergence rate and convergence accuracy.
Keywords: quantum fireworks algorithm; opposition-based learning; continuous optimisation; quantum computing; global optimum.
International Journal of Computing Science and Mathematics, 2015 Vol.6 No.3, pp.256 - 265
Available online: 28 May 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article