Parallel BMDA with an aggregation of probability models

—The paper is focused on the problem of aggregation of probability distribution applicable for parallel Bivariate Marginal Distribution Algorithm (pBMDA). A new approach based on quantitative combination of probabilistic models is presented. Using this concept, the traditional migration of individuals is replaced with a newly proposed technique of probability parameter migration. In the proposed strategy, the adaptive learning of the resident probability model is used. The short theoretical study is completed by an experimental works for the implemented parallel BMDA algorithm (pBMDA). The performance of pBMDA algorithm is evaluated for various problem size (scalability) and interconnection topology. In addition, the comparison with the previously published aBMDA [24] is presented.

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Jirí Jaros, Josef Schwarz
• Keywords: CEC 2009, Marginal Distribution Algorithm, Probability Distribution Applicable, Probability Parameter Migration