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In order to effectively deal with randomness and fuzziness in multi-objective optimization, according to the advantages of cloud model in dealing with the two phenomenon, it is combined to multi-objective optimization problem. In addition, for genetic algorithm, because of the inherent of parallel mechanism, it can ensure to obtain numbers of possible Pareto optimal solution at the same time, and it can also be able to overcome the optimization of the traditional difficulties, such as huge solution space or complex search algorithm, according to the above-mentioned advantages of genetic algorithm. Based on this, the multi-objective optimization combining cloud model and improved genetic algorithm, via cloud processing to each subgroup, by improving the fitness calibration and transform basic optimization in genetic algorithm to cloud model, then a new genetic cloud model to multi-objective optimization problem is proposed. Finally, the case study validate the effectiveness of the algorithm.
Date of Conference: 12-14 Oct. 2008