基于多近似模型的交互式遗传算法

人的疲劳向题是交互式遗传算法的核心问题,它制约了交互式遗传算法在复杂优化问题中的应用.为了解决该问题,本文提出基于多近似模型的交互式遗传算法.该算法首先将搜索空间划分,然后利用传统交互式遗传算法得到的数据,在不同子空间生成不同的近似模型,最后采用该模型近似人对进化个体的评价,从而减少人评价的数量,有效解决人的疲劳问题.算法性能分析及在服装进化设计系统中的应用验证了其有效性.     

On using genetic algorithms for optimum damper placement in space trusses

Although similar in some ways to the design of aircraft and other lightweight structures, the optimal design of space structures has several unique challenges. A flexible optimization system allowing for multiple analysis techniques and including continuous and discrete design variables is desired. Although not as computationally efficient as traditional optimization techniques, genetic algorithms meet this requirement.The present investigation used a genetic algorithm to place passive viscous dampers in space trusses. The flexibility of the system was demonstrated through the use of fixed and free boundary conditions. The results showed that four dampers are generally sufficient to suppress bending motion in a seventy-two-bar fixed truss and a seventy-eight-bar free truss. The results were intuitive, demonstrating the suitability of the genetic algorithm to this class of problem.     

A hybridization of genetic algorithms and fuzzy logic for the single-machine scheduling with flexible maintenance problem under human resource constraints

This work focuses on the problem of scheduling jobs on a single machine that requires flexible maintenance under human resource competence and availability constraints. To solve the problem we developed two fuzzy genetic algorithms that are based on respectively the sequential and total scheduling strategies. The one respecting the sequential strategy consists in two phases. In the first phase, the integrated production and maintenance schedules are generated. In the second one, the human resources are assigned to maintenance activities. The second algorithm respecting a total strategy consists in generating the integrated production and maintenance schedules that explicitly satisfy the human resource constraints. In regard to these two different strategies, we studied then two integrated fuzzy genetic algorithms that use the fuzzy logic framework to deal with the uncertain nature of both production and maintenance data. The proposed genetic algorithms have been implemented and applied to non-standard test problems which integrate production, maintenance and human resource data. The experimental results show that the consideration of human resource constraints and uncertainties allows to get more realistic and applicable solutions. Moreover, the comparison between the two proposed algorithms shows that the one based on the total strategy outperforms the one based on the sequential strategy regarding the objective functions’ optimization. However, this latter is better in terms of computational times.