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Variance analysis and adaptive control in intelligent system based on Gaussian model

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.