基于UNGO语言求解组合优化问题

LING09．0的主要功能是求解大型数学规划问题，但利用其求解组合问题尚未发现这方面的文献。本文从组合优化方面的一个经典问题——皇后问题入手，引入攻击函数。将该组合问题转化为一个线性规划问题，利用LINGO软件求解，取得了比较好的应用效果。但由于受到软件的限制。其求解规模还是受到一定的限制。     

一种求解函数优化问题的新算法

1.引言遗传算法的基本思想来源于达尔文(Dorwin)的进化论和门德尔(Mendel)的遗传学说。达尔文的进化论认为:每一物种在不断的发展过程中越来越适应环境,在个体的生存与发展中那些适应环境的个体则被保留下来,体现了“适者生存”的原理。与此相应,门德尔的遗传学说则认为:遗传是作为一种指令码封装在每个细胞中,并以基因的形式包含在染色体中。通过基因杂交和基因突变可产生对环境适应强的后代,并通过优胜劣汰的自然选择,适应值高的基因则被保留下来。霍兰德(Holland)等人正是综合了上述两种学说的基本     

基于Anytime算法的组合优化问题求解

介绍一种基于Ａｎｙｔｉｍｅ算法的组合优化问题求解框架,并报告了对ＴＳＰ问题进行求解的实验。实验结果表明,上述框架可以较好地协调２的复杂度与求解时间要求之间的冲突。     

求解车间作业调度问题的一种改进遗传算法

针对标准遗传算法收敛速度慢和易陷入局部最优的问题,在总结已有经验的基础上对标准遗传算法提出改进:采用基于工序的编码、解码方式,每一次遗传操作后对种群采用循环选择并保留最优个体,对交叉操作和变异概率的计算提出了一系列改进方法,避免遗传算法产生无用解或陷入局部优化,以提高效率。通过实验验证,改进后的算法具有可行性,并且可以得到十分满意的结果。     

解Job-shop调度问题的神经网络方法

研究用神经网络方法解决Job-shop调度问题.首先描述解Job-shop调度问题的算法,然后给出这一算法及其网络性质的理论结果.仿真实验结果证明了该方法是可行的.最后,针对几类典型调度问题的解决进一步说明了这一方法的优势.     

求解车间作业调度问题的一种改进遗传算法

针对标准遗传算法收敛速度慢和易陷入局部最优的问题，在总结已有经验的基础上对标准遗传算法提出改进：采用基于工序的编码、解码方式，每一次遗传操作后对种群采用循环选择并保留最优个体，对交叉操作和变异概率的计算提出了一系列改进方法，避免遗传算法产生无用解或陷入局部优化，以提高效率。通过实验验证，改进后的算法具有可行性，并且可以得到十分满意的结果。     

求解优化问题的新趋势——跨学科方法

一、概述我们目前所说的优化问题一般分为两类，即连续变量问题和离散变量问题。传统的处理方法常常是借助数学规划、控制理论、网络图论等领域中的工具，有相当一部分已经由这些经典方法所解决，但仍然存在一大批的优化问题迄今未能解决，主要是一些尚未找到一般规律的非线性问题以及人们在70年代整理的所谓NP-完美类问题，后者基本集中在离散型变量的优化问题中，即组合优化问题。近二三十年内，由于计算机技术的飞速发展，各学科分支的长足进步和不断扩展，求解优化问题的手段也层出不穷，其中最为醒目、最引人关注的就是来自其它一些看…     

关于求解难组合优化问题的蚁群优化算法

1.引言组合优化问题在规划、调度、资源分配、决策等工程问题中有着非常广泛的应用。在问题规模较小时,可以使用分支定界法或动态规划方法等来求解。当问题规模增大时,解的数目虽然有限,但呈指数增长,要在合理时间内求得准确的最优解实际上已不可能。为此,人们设计了各种启发式算法。近年来,最重要和最有希望的一个研究领域是构造“师法自然“的启发式。它们类比社会系统、物理系统、生物系统等的运行机制,设计算法在问题的解空间中进行非确定性搜索。典型的有遗传算法(GA)、模拟退火(SA)、人工神经网络(ANN)。这些算法由于其自适应性,对难组合优化问题的求解取得了好的结果,被广泛应用于工程优化和控制中。本文将要介绍的蚁群优化算法,由于其较强的自适应性和对问题状态的学习能力,正逐步成为一种新的有潜力的优化算法。     

基于LINGO语言求解组合优化问题

LINGO9.0的主要功能是求解大型数学规划问题,但利用其求解组合问题尚未发现这方面的文献.本文从组合优化方面的一个经典问题--皇后问题入手,引入攻击函数,将该组合问题转化为一个线性规划问题,利用LINGO软件求解,取得了比较好的应用效果.但由于受到软件的限制,其求解规模还是受到一定的限制.     

基于遗传和禁忌搜索算法求解车间调度优化问题

针对柔性生产环境下的车间调度问题,在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传和禁忌搜索的混合动态优化调度算法,并用实例对该算法进行了仿真研究。结果表明,此算法有很好收敛精度,是可行的,并且能够在扰动发生后提供新的调度计划,与传统的调度算法相比较,体现了明显的优越性。     

一种车间调度死锁实时解决算法

对比2种不同的编码形式及算法,提出一种实时发现和死锁解决算法,该算法不抛弃任何染色体,仅调整死锁染色体内基因的调度顺序,从而实现所有染色体的调度,并快速找出最优解。仿真实验结果表明,该算法是有效的。     

用Hopfield模型求解组合优化问题的拟人策略

文章对Hopfield网络状态的更新准则，提出了一个自适应的拟人策略。数值模拟表明，引入拟人策略的Hopfield模型对求解组合优化问题非常有效。     

禁忌搜索算法用于解决网络路由问题

Routing problem is a very import problem in the network design.However,with the increasing of the number of vertices,the convergence speed of the conventional method(such as the Dijkstra algorithm)becomes slow.In some services,the accurate shortest path isn't requested.This paper presents a new algorithm for solving this problem based on the tabu search technique.The tabu search algorithm can get the satisfied path with the changing of the iteration times,the tabu period and neighborhood size.Simulation results demonstrate that the proposed method is very efficient for computing the shorted path,especially when the scale of the network is large.     

Graph Approach to Solving Problems of Combinatorial Recognition

The problem of finding two radioactive balls among a given set of balls is reduced to a combinatorial recognition problem, and the latter is solved by a series of tests. Methods of graph theory are used to solve the problem. The approach is illustrated by an example with 22 balls.     

航班计划优化模型及其应用研究

以利润最大化为目标函数，构造了航空公司航班计划优化模型。采用禁忌搜索算法对其进行求解，通过对航班数据的预处理和技巧，加快了算法的处理效率，对航空公司编制航班计划具有实际意义。以某航空公司特定机型的航班计划数据进行实证，验证了该模型和算法的可行性。     

基于遗传算法的轨道交通线路排班优化研究

根据轨道交通系统的特点,考虑不同出行方式,建立基于遗传算法的轨道交通线路排班模型;并对整个算法进行设计,使其在有限的算法步骤内,找出所有满足约束条件的最优或次优解。     

Solving the uncapacitated facility location problem using tabu search

A tabu search heuristic procedure is developed to solve the uncapacitated facility location problem. Tabu search is used to guide the solution process when evolving from one solution to another. A move is defined to be the opening or closing of a facility. The net cost change resulting from a candidate move is used to measure the attractiveness of the move. After a move is made, the net cost change of a candidate move is updated from its old value. Updating, rather than re-computing, the net cost changes substantially reduces computation time needed to solve a problem when the problem is not excessively large. Searching only a small subset of the feasible solutions that contains the optimal solution, the procedure is computationally very efficient. A computational experiment is conducted to test the performance of the procedure and computational results are reported. The procedure can easily find optimal or near optimal solutions for benchmark test problems from the literature. For randomly generated test problems, this tabu search procedure not only obtained solutions completely dominating those obtained with other heuristic methods recently published in the literature but also used substantially less computation time. Therefore, this tabu search procedure has advantage over other heuristic methods in both solution quality and computation speed.     

Heuristics from Nature for Hard Combinatorial Optimization Problems

In this paper we try to describe the main characters of Heuristics 'derived' from Nature, a border area between Operations Research and Artificial Intelligence, with applications to graph optimization problems. These algorithms take inspiration from physics, biology, social sciences, and use a certain amount of repeated trials, given by one or more 'agents' operating with a mechanism of competition-cooperation. Two introductory sections, devoted respectively to a presentation of some general concepts and to a tentative classification of Heuristics from Nature open the work. The paper is then composed of six review sections: each of them concerns a heuristic and its application to an NP-hard combinatorial optimization problem. We consider the following topics: genetic algorithms with timetable problems, simulated annealing with dial-a-ride problems, sampling and clustering with communication spanning tree problems, tabu search with job-shop-scheduling problems, neural nets with location problems, ant system with travelling salesman and quadratic assignment problems.     

组合优化问题反问题的研究进展

本文重点介绍了组合优化问题反问题的研究进展。具体内容包括：线性规划问题反问题、最短路问题反问题、最小费用流问题反问题和网络容量扩充问题反问题的提出背景、研究成果、应用前景及一些可能的研究方向。     

Problem difficulty for tabu search in job-shop scheduling

Tabu search algorithms are among the most effective approaches for solving the job-shop scheduling problem (JSP). Yet, we have little understanding of why these algorithms work so well, and under what conditions. We develop a model of problem difficulty for tabu search in the JSP, borrowing from similar models developed for SAT and other NP-complete problems. We show that the mean distance between random local optima and the nearest optimal solution is highly correlated with the cost of locating optimal solutions to typical, random JSPs. Additionally, this model accounts for the cost of locating sub-optimal solutions, and provides an explanation for differences in the relative difficulty of square versus rectangular JSPs. We also identify two important limitations of our model. First, model accuracy is inversely correlated with problem difficulty, and is exceptionally poor for rare, very high-cost problem instances. Second, the model is significantly less accurate for structured, non-random JSPs. Our results are also likely to be useful in future research on difficulty models of local search in SAT, as local search cost in both SAT and the JSP is largely dictated by the same search space features. Similarly, our research represents the first attempt to quantitatively model the cost of tabu search for any NP-complete problem, and may possibly be leveraged in an effort to understand tabu search in problems other than job-shop scheduling.