蚁群遗传混合算法

将蚁群遗传混合算法分别求解离散空间的和连续空间优化问题.求解旅行商问题的混合算法是以遗传算法为整个算法的框架,利用了蚁群算法中的信息素特性的进行交叉操作;根据旅行商问题的特点,给出了4种变异策略;针对遗传算法存在的过早收敛问题,加入2-0pt方法对问题求解进行了局部优化.与模拟退火算法、标准遗传算法和标准蚁群算法进行比较,4种混合算法效果都比较好,策略D的混合算法效果最好.求解连续空间优化问题是以蚁群算法为整个算法的框架,加入遗传算法的交叉操作和变异操作,用测试函数验证了混合蚁群算法的正确性.     

邮政运输中邮路的规划和邮车调度问题的研究

以邮政运输网络中运输效益最优为目标,建立了分步规划的图论模型.运用Floyd算法、Kruskal算法对模型进行分步求解并逐步优化,通过Matlab、Lingo、SPSS软件求解,提出三种优化邮路、降低邮车调度成本的方法.模型对解决邮路问题、单旅行商、多旅行商等相关问题具有普遍适用性,可以推广到点数更多TSP的问题.     

遗传算法与蚂蚁算法的融合在带时间窗的车辆路径问题中的应用

对带时间窗的车辆路径问题(VRPTW)的求解分为两个过程,先由遗传算法求解出初步的可行解,由此生成信息素初始分布,而后采用蚂蚁算法找出问题的最优解或近似最优解.通过具体算例,从数值计算上探索了遗传算法和蚂蚁算法融合后的优化能力,获得了满意的效果.     

一种求解TSP问题的多策略改进蚁群算法

蚁群算法是一种求解复杂组合优化问题的启发式仿生进化算法,并是求解TSP问题行之有效的一种随机算法.但此算法仍存在求解精度低、易陷入局部最优及求解效率低的问题,针对该问题提出一种多策略改进蚁群算法.采用最近邻法影响初始信息素的分布,达到降低算法初期较短路径上信息素浓度的目的,并在转移规则变异调整的基础上,结合路径的均值交叉进化策略,增强算法探索全局解空间和避免陷入局部最优的能力.然后,结合迭代和精英策略对信息素更新机制进行改进,进一步提高化算法的求解性能及求解效率,最后,对从TSPLIB数据库选出的8个实例进行求解并与其他算法进行对比,实验结果表明,改进算法在求解旅行商问题时的高效性,且具有较高的运算性能.     

基于客户满意度的车辆路径问题及蚁群算法求解北大核心

针对传统车辆路径问题片面强调行驶里程最短的弊端,引入客户满意度目标,提出了基于客户满意度的车辆路径问题数学模型,并通过线性加权将多目标模型转化为单目标.使用蚁群算法求解模型,并在蚂蚁状态转移中引入时间窗宽度因素,以优先考虑那些具有时间紧迫性的客户.对Solomon案例的实验仿真,结果表明了模型的合理性和算法的高效性.     

基于客户满意度的车辆路径问题及蚁群算法求解

针对传统车辆路径问题片面强调行驶里程最短的弊端,引入客户满意度目标,提出了基于客户满意度的车辆路径问题数学模型,并通过线性加权将多目标模型转化为单目标.使用蚁群算法求解模型,并在蚂蚁状态转移中引入时间窗宽度因素,以优先考虑那些具有时间紧迫性的客户.对Solomon案例的实验仿真,结果表明了模型的合理性和算法的高效性.     

基于不同条件的旅游路线规划问题研究

运用2015年全国研究生数学建模竞赛F题的数据资料,针对旅游路线合理规划问题的第一问展开研究.以F题的问题一为起点进行了分析研究,是因为第一问的完成是解决后续问题的关键.首先通过地图搜集并补全了缺失数据并对数据进行合理的处理,然后采用将旅游年数最少目标转化为该最小生成树的"最少圈覆盖"方法来进行对问题一的求解,也可以理解为一个广义的多旅行商问题,以旅行商的人数(即年数)最少为目标.采用Dijkstra算法、最少圈覆盖法、智能算法和图论聚类等方法,通过这些方法建立了单目标优化模型,并运用旅行商问题和模型之间的转换来对问题进行分析与求解.     

基于模拟退火算法的充电路径规划

主要利用模拟退火算法解决针对无线传感器网络的充电器路径规划问题,并求得网络中每个传感器对应的最小电池容量.该实际问题可抽象为经典旅行商问题(TSP)以及多旅行商问题(MTSP).针对中小规模的TSP问题,以总路程最小为优化目标,利用模拟退火算法搜索全局最优解;针对MTSP问题,以多条路径中最长的路程和每条支路平均路程的加权之和为优化目标,利用模拟退火算法进行求解.本文将最小电池容量模型简化为线性函数进行求解,并按照实际情况设计部分参数数值和部分参数取值范围,得到每个传感器最小电池容量的具体数值.     

一种改进的混合型蚁群算法在TSP问题中的应用

介绍了一种求解TSP问题的算法改进的混合型蚁群算法,该算法在近邻法构造初始解的基础上,使用2-opt局部搜索法对当前解进行改进,在更新全局信息素时采用基于排序的蚂蚁系统对排在前2名的蚂蚁更新全局信息素,且为全局信息素设置最大值和最小值,并使用Matlab仿真求解了kroa200等13个经典tsp问题,得到的结果和最优解的误差很小,并和两种最新改进的蚁群算法以及两种自组织算法进行比较,比较结果充分证明了该改进算法的有效性.     

多层防御模式下武器目标分配决策的群体智能优化算法

武器目标分配(WTA)是军事运筹学中经典的NP完全问题,迄今为止未找到求精确解的多项式时间算法.针对武器数量、布防空间、运行维护成本以及人力资源等多约束下的多层防御WTA问题,采用粒子群优化(PSO)和蚁群优化(ACO)两种群体智能算法求解.给出了PSO和ACO算法实现方案,通过一个算例评估两个算法的性能.结果表明,两种算法都能给出高质量的近似最优解,对求解WTA问题是有效的.PSO在解的质量、算法鲁棒性和计算效率方面均优于ACO.     

多目标0-1规划问题的蜂群算法

针对多目标0-1规划问题,本文给出一种新型的智能优化算法——蜂群算法进行求解,并通过实例验证,与遗传算法、蚁群算法和元胞蚁群算法作了相应比较。就多目标0-1规划问题而言,蜂群算法能得到更多的Pareto解,说明了蜂群算法在解决该类问题上的有效性。     

Multi Colony Ant Algorithms

In multi colony ant algorithms several colonies of ants cooperate in finding good solutions for an optimization problem. At certain time steps the colonies exchange information about good solutions. If the amount of exchanged information is not too large multi colony ant algorithms can be easily parallelized in a natural way by placing the colonies on different processors. In this paper we study the behaviour of multi colony ant algorithms with different kinds of information exchange between the colonies. Moreover we compare the behaviour of different numbers of colonies with a multi start single colony ant algorithm. As test problems we use the Traveling Salesperson problem and the Quadratic Assignment problem.     

基于人机交互—蚁群算法的港口疏船调度优化

为解决因港口无法正常作业导致大量船舶压港后的疏船调度问题,从同时兼顾船公司和港口方利益出发,建立了船舶平均在港时间最短、额外作业成本最低、生产秩序恢复最快的调度生产多目标优化模型。利用多属性效用理论将多目标转换为单目标,并构建了相应的评价函数,采用改进的蚁群算法并结合人机交互以及邻域搜索方法求解,最后以大连港集装箱码头实际案例进行验证。结果表明,与通常调度方法相比,文中建立的优化模型能够更好地解决疏船问题;对比常规的蚁群算法,改进后的算法搜索效率更高。上述模型和算法为集装箱码头的生产组织调度提供了新的优化思路和方法。     

模糊蚁群算法及其在TSP中的应用

在传统蚁群算法的基础上加入了使用模糊规则表更新信息素的策略,提出了一种新的算法——模糊蚁群算法.算法结合了模糊控制中输入输出的模糊化处理和蚁群寻优的特点,为实际问题提供了新的解决手段.文中将模糊蚁群算法应用于TSP问题,通过对中国31个省会城市等实例数据进行的测试,验证表明了新算法具有良好的有效性和鲁棒性.     

Metaheuristics approach for solving personalized crew rostering problem in public bus transit

The crew rostering problem in public bus transit aims at constructing personalized monthly schedules for all drivers. This problem is often formulated as a multi-objective optimization problem, since it considers the interests of both the management of bus companies and the drivers. Therefore, this paper attempts to solve the multi-objective crew rostering problem with the weighted sum of all objectives using ant colony optimization, simulated annealing, and tabu search methods. To the best of our knowledge, this is the first paper that attempts to solve the personalized crew rostering problem in public transit using different metaheuristics, especially the ant colony optimization. The developed algorithms are tested on numerical real-world instances, and the results are compared with ones solved by commercial solvers.     

A particle swarm optimization-based hybrid algorithm for minimum concave cost network flow problems

Traditionally, minimum cost transshipment problems have been simplified as linear cost problems, which are not practical in real applications. Some advanced local search algorithms have been developed to solve concave cost bipartite network problems. These have been found to be more effective than the traditional linear approximation methods and local search methods. Recently, a genetic algorithm and an ant colony system algorithm were employed to develop two global search algorithms for solving concave cost transshipment problems. These two global search algorithms were found to be more effective than the advanced local search algorithms for solving concave cost transshipment problems. Although the particle swarm optimization algorithm has been used to obtain good results in many applications, to the best of our knowledge, it has not yet been applied in minimum concave cost network flow problems. Thus, in this study, we employ an arc-based particle swarm optimization algorithm, coupled with some genetic algorithm and threshold accepting method techniques, as well as concave cost network heuristics, to develop a hybrid global search algorithm for efficiently solving minimum cost network flow problems with concave arc costs. The proposed algorithm is evaluated by solving several randomly generated network flow problems. The results indicate that the proposed algorithm is more effective than several other recently designed methods, such as local search algorithms, genetic algorithms and ant colony system algorithms, for solving minimum cost network flow problems with concave arc costs.     

A multi-objective multi-population ant colony optimization for economic emission dispatch considering power system security

With increasing concern about global warming and haze, environmental issue has drawn more attention in daily optimization operation of electric power systems. Economic emission dispatch (EED), which aims at reducing the pollution by power generation, has been proposed as a multi-objective, non-convex and non-linear optimization problem. In a practical power system, the problem of EED becomes more complex due to conflict between the objectives of economy and emission, valve-point effect, prohibited operation zones of generating units, and security constraints of transmission networks. To solve this complex problem, an algorithm of a multi-objective multi-population ant colony optimization for continuous domain (MMACO_R) is proposed. MMACO_R reconstructs the pheromone structure of ant colony to extend the original single objective method to multi-objective area. Furthermore, to enhance the searching ability and overcome premature convergence, multi-population ant colony is also proposed, which contains ant populations with different searching scope and speed. In addition, a Gaussian function based niche search method is proposed to enhance distribution and accuracy of solutions on the Pareto optimal front. To verify the performance of MMACO_R in different multi-objective problems, benchmark tests have been conducted. Finally, the proposed algorithm is applied to solve EED based on a six-unit system, a ten-unit system and a standard IEEE 30-bus system. Simulation results demonstrate that MMACO_R is effective in solving economic emission dispatch in practical power systems.     

Hybrid Population-Based Algorithms for the Bi-Objective Quadratic Assignment Problem

We present variants of an ant colony optimization (MO-ACO) algorithm and of an evolutionary algorithm (SPEA2) for tackling multi-objective combinatorial optimization problems, hybridized with an iterative improvement algorithm and the robust tabu search algorithm. The performance of the resulting hybrid stochastic local search (SLS) algorithms is experimentally investigated for the bi-objective quadratic assignment problem (bQAP) and compared against repeated applications of the underlying local search algorithms for several scalarizations. The experiments consider structured and unstructured bQAP instances with various degrees of correlation between the flow matrices. We do a systematic experimental analysis of the algorithms using outperformance relations and the attainment functions methodology to asses differences in the performance of the algorithms. The experimental results show the usefulness of the hybrid algorithms if the available computation time is not too limited and identify SPEA2 hybridized with very short tabu search runs as the most promising variant. This research was mainly done while Luís Paquete and Thomas Stützle were with the Intellectics Group at the Computer Science Department of Darmstadt University of Technology, Germany     

An Ant Colony Optimization Algorithm for Shop Scheduling Problems

We deal with the application of ant colony optimization to group shop scheduling, which is a general shop scheduling problem that includes, among others, the open shop scheduling problem and the job shop scheduling problem as special cases. The contributions of this paper are twofold. First, we propose a neighborhood structure for this problem by extending the well-known neighborhood structure derived by Nowicki and Smutnicki for the job shop scheduling problem. Then, we develop an ant colony optimization approach, which uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions. We compare this algorithm to an adaptation of the tabu search by Nowicki and Smutnicki to group shop scheduling. Despite its general nature, our algorithm works particularly well when applied to open shop scheduling instances, where it improves the best known solutions for 15 of the 28 tested instances. Moreover, our algorithm is the first competitive ant colony optimization approach for job shop scheduling instances.