An e?ective evolutionary algorithm for the multiple container packing problem

This paper focuses on a new optimization problem, which is called “The Multiple Container Packing Problem (MCPP)” and proposes a new evolutionary approach for it. The proposed evolutionary approach uses “Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA)” as a basic framework and it incorporates a heuristic local improvement approach into ALA-EA. The first step of the local search algorithm is to raise empty space through the exchange among the packed items and then to improve the fitness value through packing unpacked items into the raised empty space. The second step is to exchange the packed items and the unpacked items one another toward improving the fitness value. The proposed algorithm is compared to the previous evolutionary approaches at the benchmark instances (with the same container capacity) and the modified benchmark instances (with different container capacity) and that the algorithm is proved to be superior to the previous evolutionary approaches in the solution quality.     

启发式教学法在结构力学教学中的体会

启发式教学法符合学生自身的认知发展规律,能够调动学生思维,引导学生生动活泼地学习,并培养独立思考能力。通过结构力学课堂教学的体会,从问题启发、形象启发、讨论启发、提示启发、计算机辅助启发、作业启发等六个方面进行了一些尝试,收到了比较好的课堂效果。     

寻找运输问题初始可行解的启发式方法

针对在单位运价表格中含有数值相近的元素的运输问题,提出了一种能有效降低目标函数值,以减少求解过程中迭代次数的启发式方法.     

求解带平衡约束矩形布局问题的混合算法

以卫星舱布局为背景,针对二维带平衡约束的矩形集在圆容器内的布局问题,提出了一种混合算法,即带压缩策略的动态匹配算法,并与粒子群优化算法结合起来.对于给定的圆容器,将整个布局过程分为4个阶段,相应地将圆容器分成8个区域.每次放置矩形时,动态匹配算法先根据系统质心动态选择装填区域,然后在该装填区域中根据排序函数选择待布矩形.压缩策略用来压缩圆容器,粒子群优化算法用来优化排序函数的参数.在测试集上的计算结果表明:相对于已有算法,提出的混合算法更加有效.     

基于FM信号的外辐射源雷达组网系统多传感器选择算法

提出一种基于调频广播(FM)信号的外辐射源雷达组网系统多传感器快速启发式算法,目的是使系统能够动态地协调各部FM信号接收机的使用,进而在资源有限的条件下达到更好的目标参数估计性能。该算法采用相干克拉美罗界(CRLB)作为代价函数,并通过贪婪快速启发式算法对优化问题进行了求解。仿真结果表明,所提算法不仅可以在系统资源有限的情况下提升目标的参数估计性能,而且可以大大减小计算量,具有很强的实用性。     

复杂FMS系统的启发式调度策略

用Petri网对柔性制造系统进行建模的基础上,提出了一种针对较大规模柔性制造系统的启发式调度优化算法.采用了改进的子节点处理方法、与派遣规则相结合的启发函数以及移动窗口等策略,弥补了算法在节点质量评价和节点搜索方面的不足,有效减小算法在可述图中寻路时对节点的搜索范围,加快了算法的收敛速度.仿真结果表明:相对于其他调度算法,该文算法可以快速、高效的编制系统调度计划.     

Dynamic Air-Route Adjustments——Model,Algorithm,and Sensitivity Analysis

Dynamic airspace management (DAM) is an important approach to extend limited air space resources by using them more efficiently and flexibly.This paper analyzes the use of the dynamic air-route adjustment (DARA) method as a core procedure in DAM systems.DARA method makes dynamic decisions on when and how to adjust the current air-route network with the minimum cost.This model differs from the air traffic flow management (ATFM) problem because it considers dynamic opening and closing of air-route segments instead of only arranging flights on a given air traffic network and it takes into account several new constraints,such as the shortest opening time constraint.The DARA problem is solved using a two-step heuristic algorithm.The sensitivities of important coefficients in the model are analyzed to determine proper values for these coefficients.The computational results based on practical data from the Beijing ATC region show that the two-step heuristic algorithm gives as good results as the CPLEX in less or equal time in most cases.     

Model and algorithm of flow interception problem with multi-type of flows

To study simultaneous location of different kinds of facilities, a new model of flow interception problem with multi-type of flows is proposed, with the consideration of multi-purpose flows and the influence of facilities on each other. To be more practical, the objective is to maximize the benefits from flow-by customers instead of maximizing flows in the past. Since this problem is NP-hard and there is no optimal solution for large network, greedy and improved greedy heuristic algorithms are proposed. A computational example is presented to show that the optimal location decisions are proved to be strongly dependent on the influence coefficients and average profits by one customer and different types of facilities are almost co-located. Finally the results of sensitivity analysis are reported.     

一种多目标增量启发式搜索算法

提出一种多目标增量启发式搜索算法, 该算法结合启发式搜索与增量搜索的思想, 当多目标问题搜索图的状态格局发生改变时, 该算法并不是对变化后的问题进行完全重新求解, 而是部分利用了先前搜索保留的信息求解新问题的最优解集, 从而提高了问题求解的效率. 通过Gridworld标准测试问题上的实验测试, 验证了算法的效率.     

TS求解多机成组工件调度

讨论并行多机成组工件的极小化最大通过时间调度问题·它是一个非常复杂的组合优化问题,是NP难题·为了解决此类问题,采用了两种智能优化算法方法·通过大量的仿真实验,将两种方法进行了比较·结果证明:禁忌搜索结合启发式的智能优化算法可靠性高、运算速度快,有能力有效地解决大规模实际问题·