Human-Computer Interactive Genetic Algorithm and Its Application to Constrained Layout Optimization
Human-Computer Interactive Genetic Algorithm and Its Application to Constrained Layout Optimization
复制标题
DOI：
--
复制
发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Qianying Zhi
中科院分区：
文献类型：
--
作者：
Qianying Zhi
具有行为约束（惯性、平衡、稳定性、振动等）的方案设计和布局问题以及约束布局优化问题属于NPC。近年来，它们越来越受到人们的关注，并在航天器、船舶、车辆、机床、机器人等各种应用领域中出现。本文以人造卫星舱室布局设计为背景，提出了一种人机交互遗传算法，用于求解二维约束布局优化问题。该算法首先将人工个体作为染色体群体的一部分，并将其划分为若干个子群体；其次，每个子群独立运行，其交叉和变异算子的值与其他子群不同。经过复制、交叉和突变操作，每个子群中较好的个体被转移到相邻的子群中。第三，人类专家通过多代循环来检验局部最优解，并利用可视化技术设计新的人工智能。顺便说一下，他是根据适应度函数的值来找到这些新的人工智能的，然后是根据许多实际的工程因素。最后对这些新的ai进行复制，以确保它们在染色体群体中发挥重要作用。然后将它们放入染色体群中以取代较差的个体。重复上述四个步骤，直到人类专家找到满意的解决方案。进而形成人机交互遗传算法，解决实际工程布局问题，最大限度地发挥人与计算机各自的特长。三个算例（其中一个由作者提出，并已知其最优解）的结果表明，该算法是可行和有效的。人机交互遗传算法为解决工程中实际复杂的布局问题以及人机交互的可操作性问题提供了一种有效的方法。
Scheme design and packing problems with behavioral constraints (inertia, balance, stability and vibration etc.) and constrained layout optimization problems belong to NPC. They are concerned more and more in recent years and arise in a variety of application areas such as the layout design of spacecraft, shipping, vehicle, machine tool, and robot etc. Taking the layout design of artificial satellite cabins as background, a human computer interactive genetic algorithm is proposed for solving the two dimensional constrained layout optimization problems in this paper. Firstly, the algorithm makes the artificial individuals (AIs) as a part of the chromosome population and divides the population into some subgroups. Secondly, each subgroup, whose values of crossover and mutation operators are different from other subgroups, operates independently. After copy, crossover, and mutation operations, the better individual in each subgroup is transferred to adjacent subgroups. Thirdly, human expert examines the locally optimal solution that can be obtained through the loops of many generations and designs new AIs with visualized technology. By the way, he finds these new AIs according to the value of fitness function, then to many actual engineering factors. Finally these new AIs are copied in order to ensure that they play an important role in chromosome population. Then they are placed into the chromosome population to replace the worse individuals. The four steps mentioned above are repeated until the human expert finds the satisfied solution. And then human computer interactive genetic algorithm is formed to solve the practical engineer layout problems and the specialties of human and computer can be exerted to the utmost respectively. The results of three examples(one of them is proposed by the authors, and its optimal solution is known)show that this algorithm is feasible and efficient. The human computer interactive genetic algorithm provides an effective approach for the practically complex layout problems in engineering, as well as for the problems of maneurerability of human computer interaction.