Development, Evaluation and Application of Multi-objective Optimization Methods by Genetic Algorithms

1.多重编码遗传算法（MCGA）是一种多目标遗传算法，每个个体只对多个目标中的一个进行专门化，并根据每个目标进行不同的编码。通过计算机实验与传统的并行选择方法进行了比较，结果表明两者在搜索性能上具有互补性，因此提出了一种混合的并行选择方法.将该方法应用于两目标背包问题和两目标流水车间调度问题，并与顺序并行选择算法进行了性能比较。(1)在背包问题的应用中，以系统的方式生成具有不同相关系数的数据集，以控制Pareto集的分布。(2)在流水车间调度的应用中，针对每个目标提出了启发式解码方法.通过相对精度、覆盖率、多样性和获得解的个数对所获得的解进行定量评价.仿真结果表明，该方法能有效地获得不同的解，特别是在Pareto集较大的情况下，这可能是由于问题的统计特性、问题规模较大或解空间维数较高造成的.

1. Multiple Coding Genetic Algorithm (MCGA) is proposed as a multi-objective GA method, in which each individual specializes on one of many objectives and has a different decoding according each objective. The comparison with a conventional parallel selection method by computer experiment shows the complementary performance in searching, therefore a hybrid method of the both is also proposed.2. The proposed method is applied 2-objective knapsack problems and 2-objective flowshop scheduling problem, and its performance is compared with the ordinal parallel selection.(1) In the application to knapsack problems, the data sets with different correlation coefficients are generated in a systematic way to control the distribution of a Pareto set.(2) In the application to flowshop scheduling, heuristic decoding methods are proposed for each objective.3. Quantitative evaluations of the obtained solutions are given by relative accuracy, cover ratio, diversity and the number of acquired solutions.4. Simulation result shows that the proposed method is effective to obtain diverse solutions especially in the problem with a large Pareto set, which results either from a statistical character of a given problem, from a largeness of a problem scale, or from a high dimensionality of a solution space.