Authors: Jun Liu; Yishou Wang; Hongfei Teng
Addresses: School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China ' State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China ' School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Abstract: Estimation of distribution algorithms (EDAs) are intelligent systems that take advantage of statistical learning techniques. The distribution of promising regions in the search space is estimated and probabilistically guide the new particles' searching towards them in EDAs. But EDAs cannot solve complex optimisation problems reliably and efficiently because of premature convergence and difficulty of complex probabilistic model learning. This paper presents a PCA-EDA algorithm which introduces principal component analysis (PCA) into simple Gaussian model-based EDA. PCA-EDA is aimed to keep the balance of accuracy and efficiency of probabilistic model learning, as well as to avoid premature convergence by PCA's ability of analysis of Gaussian model's variance. From the results of numerical experiments, it is showed that the proposed method is feasible and effective for complex optimisation problems.
Keywords: principle component analysis; PCA; estimation of distribution algorithm; EDA; intelligent systems; adaptive variance control; variance analysis; adaptive control; Gaussian model; complex optimisation.
International Journal of Modelling, Identification and Control, 2013 Vol.18 No.1, pp.26 - 33
Available online: 05 Feb 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article