Histogram-based estimation of distribution algorithm with RPCL clustering in continuous domain

Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. Nowadays, histogram probabilistic model has become a hot topic in the field of estimation of distribution algorithms because of its intrinsic multimodality that makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram probabilistic model more efficiently explore and exploit the search space, rival penalized competitive learning (RPCL) clustering was brought into the algorithm, so that the algorithm could use the knowledge about distribution of values belong to each span. Experimental results showed that the improved algorithm in this paper can give comparable with or better performance than those improved algorithms.