Cloud Model-Based Multi-objective Estimation of Distribution Algorithm with Preference Order Ranking

Estimation of distribution algorithms(EDAs) are a class of evolutionary optimization algorithms. In this paper, EDAs scheme are extended to multi-objective optimization problems by using preference order and cloud model. In the algorithm, three digital characteristics from the current population are firstly estimated by backward cloud generator. Afterwards, forward cloud generator used to generate current offsprings population according to three digital characteristics. The population with the current population and current offsprings population is sorted based on preference order, and the best individuals are selected to form the next population. The proposed algorithm is tested to compare with some other algorithms using a set of benchmark functions. The experimental results show that the algorithm is effective on the benchmark functions.