CAJ | 학술논문

为了有效求解多目标优化问题，找到分布宽广、均匀的Pareto解集，提出了一个基于空间网格划分的进化算法。将目标空间网格化，利用网格的位置，删除大量被支配个体。在杂交算子中利用了单个目标最优的个体信息，以增加非劣解的宽广性。利用一种新设计的基于最大距离排序的方法删除非劣解集中多余个体。数值实验表明提出的算法是可行有效的。
위료유효구해다목표우화문제，조도분포관엄、균균적Pareto해집，제출료일개기우공간망격화분적진화산법。장목표공간망격화，이용망격적위치，산제대량피지배개체。재잡교산자중이용료단개목표최우적개체신식，이증가비렬해적관엄성。이용일충신설계적기우최대거리배서적방법산제비렬해집중다여개체。수치실험표명제출적산법시가행유효적。
In order to solve the multi-objective optimization problem effectively and find a set of Pareto solutions with uniform distribution and wide range, this paper proposes an evolutionary algorithm based on a space-gridding search tech-nique. The decision space is divided into grids, and a large number of dominant individuals are deleted by using the loca-tion of the grids. In the crossover operator, the information of optimal individuals for each objective function is used to increase the range of Pareto front. A new designed method based on maximum distance sorting is applied to delete the unwanted individuals in non-dominant solution sets. Numerical experiments show that the proposed algorithm is feasible and efficient.