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Since estimation of distribution algorithms (EDAs) were proposed, many methods have been made to improve EDAs’ performance. In this paper, a variable classification-based cooperative estimation of distribution algorithm (VC-CEDA) is proposed to balance the global and local searching ability of estimation of distribution algorithms. This study proposes a variable classification method to improve the efficiency and accuracy of probability model development. And a cooperative particle swarm optimizer that can improve the local searching ability is proposed. The VC-CEDA combines the advantages of traditional algorithm and cooperative operations. VC-CEDA shows significantly better performance on 10 test functions. In this study, the experimental results show that the VC-CEDA performs better than traditional EDAs.

Variable Classification-based Co-estimation of Distribution Algorithm Assisted by a Cooperative Particle Swarm Optimizer