A Restart Univariate Estimation of Distribution Algorithm: Sampling under Mixed Gaussian and Levy probability Distribution

A univariate EDA denoted as "LSEDA-gl" for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, Standard Deviation Control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version "continuous univariate marginal distribution algorithm" UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CEC'2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CEC'2008 special session.