CAJ | 학술논문

论文给出了一个基于云模型和利用Favour排序的多目标优化算法，其新颖之处是依据云模型理论估计好解区域和新解的生成。该算法利用优化过程中获得的信息构建好解区域的云模型并用逆向云发生器估计该云模型的3个数字特征；之后，依据这3个数字特征，用正向云发生器生成当前子代种群，并用Favour排序对当前种群和当前子代种群的并集进行排序，然后，依据排序结果选择最好的一些个体形成下一代。该算法与其他算法就一组基准函数进行了测试比较，结果表明该算法更有效。
논문급출료일개기우운모형화이용Favour배서적다목표우화산법，기신영지처시의거운모형이론고계호해구역화신해적생성。해산법이용우화과정중획득적신식구건호해구역적운모형병용역향운발생기고계해운모형적3개수자특정；지후，의거저3개수자특정，용정향운발생기생성당전자대충군，병용Favour배서대당전충군화당전자대충군적병집진행배서，연후，의거배서결과선택최호적일사개체형성하일대。해산법여기타산법취일조기준함수진행료측시비교，결과표명해산법경유효。
A multi-objective optimization algorithm inspired from cloud model and using favour ranking is introduced .The innovation of the algorithm lies in the estimation of good solution regions and new solu -tion production according to the cloud model theory .The algorithm used information obtained during opti-mization to build the cloud model for good solution regions , and estimated three digital characteristics of the cloud model by backward cloud generators .Afterwards, forward cloud generators were used to gener-ate current offsprings population according to three digital characteristics .The population with the current population and current offsprings population was sorted using favour ranking , and the best individuals were selected to form the next population .Regarding a set of benchmark functions , the proposed algo-rithm was tested and compared with some other algorithms .The experimental results show that the algo-rithm is effective in the benchmark functions .