Multiple-Deme Parallel Estimation of Distribution Algorithms: Basic Framework and Application

This paper presents a basic framework that facilitates the development of new multiple-deme parallel estimation of distribution algorithms (PEDAs). The aim is to carry over the migration effect that arises in multiple-deme parallel genetic algorithms (PGAs) into probability distribution of EDAs. The idea is to employ two kinds of probability vector (PV): one each for resident and immigrant candidates. The distribution of crossbred individuals (that virtually exist on both kinds of PV) is then utilized by a new type of crossover, the PV-wise crossover. A multiple-deme parallel population-based incremental learning (P2BIL) scheme is proposed as an application. The P2BIL scheme closely follows the proposed framework that includes a new learning strategy (i.e., PV update rule). Experimental results show that P2BIL generally exhibits solutions that compare favourably with those computed by an existing PGA with multiple demes, thereby supporting the validity of the proposed framework for designing multiple-deme PEDAs.

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