How effective are heuristic solutions for electricity planning in developing countries

Heuristic algorithms have been widely used to provide computationally feasible means of exploring the cost effective balance between grid versus off grid sources for universal electrification in developing countries. By definition in such algorithms however, global optimality is not guaranteed. We present a computationally intensive but globally optimal mixed integer non-linear programming (MINLP) model for electricity planning and use it in a Monte Carlo simulation procedure to test the relative performance of a widely used heuristic algorithm due to [28]. We show that the overall difference in cost is typically small suggesting that the heuristic algorithm is generally cost effective in many situations. However we find that the relative performance of the heuristic algorithm deteriorates with increasing degree of spatial dispersion of unelectrified settlements, as well as increasing spatial remoteness of the settlements from the grid network, suggesting that the effectiveness of the heuristic algorithm is context specific. Further, we find that allocation of off grid sources in the heuristic algorithm solution is often significantly greater than in the MINLP model suggesting that heuristic methods can overstate the role of off-grid solutions in certain situations.     

Estimation of interregional input–output table using hybrid algorithm of the RAS method and real-coded genetic algorithm

In this paper, we propose a method for improving the accuracy of the estimation of interregional input–output tables, by combining the RAS method and the real-coded Genetic Algorithm (GA); these are simple representative methods for the estimation of an interregional input–output table. By comparing the performance evaluation results obtained using the proposed method, the RAS method, and Simulated Annealing, we verified that the combination of the genetic algorithm and the RAS method can enhance the estimation accuracy of an interregional input–output table. In addition, performance is further enhanced by adjusting GA parameters.     

基于遗传算法轿车焊装车间车辆路径优化

遗传算法是一种模拟自然进化过程搜索最优解的方法。通过建立某轿车焊装车间车辆路径问题数学模型,然后利用遗传算法求解该问题,最后在Matlab软件中进行编程求解,有效地求解出问题的最优解或近似最优解。     

B2C电子商务中带退货的多配送站点车辆路径优化问题研究

研究了一个带退货的多配送站点车辆路径优化问题,建立了求解该问题的0-1整数规划模型,以实现各个配送站点在给他们客户配送货物的同时,沿途取回这些客户的退货,以节约运输成本的目的。设计了一种新的解的表示方法和邻域变换,以此为基础构造出直接求解多配送站点车辆路径优化问题的禁忌搜索算法,在该禁忌搜索算法中首先通过一个启发式算法求得了0-1整数规划模型的一个较好的初始可行解,从而提高了禁忌搜索算法的收敛速度及性能,最后通过例子对禁忌搜索算法及其性能进行了说明。     

考虑地产价格的多目标物流配送中心选址模型与求解

首先对传统的配送中心选址模型进行改进,引入地产价格因素,以建设投资成本与货物运输费用最小为目标,建立多目标选址模型。其次,利用理想点的适应度计算方法将多目标问题转化为单目标问题进行遗传算法的求解。最后,以某企业在城市的物流配送中心选址为例,对模型进行应用,算例结果验证了模型的实用性与有效性。     

基于遗传算法的油料装备维修保障资源调度研究

通过对战时油料装备维修保障资源的调度分析,利用遗传算法建立了数学模型并进行了实例仿真,在资源调度效益最佳的基础上得出了相应的维修资源调度方案。     

基于改进遗传算法的FIR数字滤波器的优化设计

提出了利用基于BP(Back Propagation)神经网络的遗传算法来设计FIR数字滤波器的方法。针对遗传算法很难实现全局最优和搜索速度比较慢的缺陷,提出了改进算法,该算法充分利用了遗传算法的全局搜索功能强和BP神经网络的搜索效率高,优化了搜索时间,提高了算法性能,对于解决大规模多极值优化问题特别有效。最后,以设计低通滤波器的实例验证算法的可行性。     

一种分层A~*路径搜索算法及性能

基于层次道路网提出了一种分层A*路径搜索算法,其特点是在同一层路网中采用A*算法,而在层次切换时采用一种启发式定向搜索入口节点的方法。既顾及了道路等级性和朝着目标行进等驾驶员的心理因素,又可以获得比传统算法更快的求解速度。实验表明,该算法是可行的、高效的。     

一种改进的松弛迭代图像配准算法

本文展示了一种改进的松弛迭代图像配准算法,此算法首先使用归一化自相关运算求得图像的粗匹配对,接着使用改进的松弛迭代匹配算法进行精匹配。本文通过优化筛选第一轮迭代结果,使得后续迭代时间明显减少。实验证明此算法效率比原始算法有了很大的提高。     

改进的动态反馈负载均衡算法

本文提出了一种改进的动态反馈负载均衡算法,在负载均衡过程中既考虑后台服务器的当前负载,又考虑后台服务器的处理能力,根据每台服务器当前负载比例值的大小合理分配用户请求,进而调整服务器间处理请求的比例,避免服务器超载时依然收到大量的请求,使平均应答时延和吞吐量等获得较好的性能,集群的整体性能得到提高。