An Optimization Algorithm Based on Cloud Model

Abstract

Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. In this paper, we introduce a new optimization method inspired from cloud model theory. The innovations of the algorithm are the estimation of good solution regions and new solution production according to the cloud model theory. First, the algorithm uses information obtained during optimization to build cloud model of good solution regions, and calculates three digital characteristics of the cloud model by backward cloud generator. Second, three digital characteristics of the cloud model are used to produce new solutions by forward cloud generator. Then, the best solutions are selected from current solutions and new solutions to form next population. The proposed algorithm is applied to some well-known benchmarks. The relative experimental results show that the algorithm is effective and achieves better solutions.