基于改进型蚁群算法的最优路径问题求解

如何高效的向用户提供最优路径是蚁群算法大规模应用于导航系统的关键问题,针对现有最优路径问题研究中蚁群算法收敛速度慢及容易发生停滞的缺点,利用A*算法的启发式信息改进蚁群算法的路径选择策略,加快算法收敛速度.同时引入遗传算法的双种群策略和蚁群系统信息素更新策略,增加全局搜索能力,避免算法出现停滞现象.仿真实验结果表明,该改进算法具有较好的稳定性和全局优化性,且收敛速度较快.     

一种物流配送车辆路径智能优化算法研究

针对遗传算法局部搜索能力不足,运行效率较低的缺点,论文提出将最速下降法与遗传算法相结合构成混合遗传算法.通过对物流配送车辆路径的特点分析,建立了物流配送车辆路径优化问题数学模型,利用改进的混合遗传算法对模型进行求解.仿真实验结果表明,混合遗传算法求解物流配送路径优化问题,可以较好地克服遗传算法局部搜索能力方面的不足和最速下降法在全局搜索能力方面的不足,得到质量较高的解.     

交互式遗传算法的改进方法及应用

针对交互式遗传算法中收敛速度慢和容易陷入局部收敛的缺点,提出遗传算法算子的一些改进策略,即利用定位部分优良基因方法,使这些基因较好地遗传到下一代。改进的算法能有效减少无效的交叉操作,收敛速度、全局搜索能力和局部搜索能力比交互式遗传算法均得到了较大的提高。将改进的算法应用于服装设计中,实验结果证明了改进后的算法在平均收敛代数和收敛到最优解的概率都优于遗传算法。     

一种车辆路径规划的改进混合算法

针对物流活动中需要找出各个配货节点之间的最短路径,用以指导物流车辆调度的问题,提出一种将遗传算法与BP神经网络相结合的新方法,规划车辆的路径,达到节约运送成本的目标。对遗传算法进行了改进,克服了遗传算法局部搜索能力差、易早熟和总体可行解质量不高的缺点。该混合算法有效弥补了遗传算法的不足,同时在遗传优化操作中引入最优保存策略,并在选择操作中采用锦标赛选择法,使算法的效率和功能得到了很大提高。通过对基于遗传算法的改进混合算法求解车辆路径优化问题的性能进行仿真,并与自适应遗传算法和免疫遗传算法进行对比分析,验证了改进混合算法的优点和有效性。     

带时间窗车辆路径问题的多目标文化基因算法

针对物流配送中带时间窗的车辆路径问题,以最小化车辆使用数和行驶距离为目标,建立了多目标数学模型,提出了一种求解该问题的多目标文化基因算法。种群搜索采用遗传算法的进化模式和Pareto排序的选择方式,局部搜索采用禁忌搜索机制和存储池的结构,协调两者得到的Pareto非占优解的关系。与不带局部搜索的多目标遗传算法和单目标文化基因算法的对比实验表明,本文算法的求解质量较高。     

基于k-shell的复杂网络最短路径近似算法

复杂网络最短路径经典算法的处理效率较低，不适用于大规模复杂网络，而现有近似算法通用性有限，且计算准确率不理想，不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此，提出基于[k]-shell的复杂网络最短路径近似算法。算法利用节点的[k]-shell值进行网络划分并引导搜索路径，利用超点聚合处理[k]-shell子网来降低路径搜索中节点和连边的规模，通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明，算法通用性较好，在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。     

模糊粒子群优化算法的第四方物流运输时间优化

针对第四方物流运输(4PL)过程中的运输时间优化问题,本文建立了第四方物流运输时间优化模型,并设计引入收敛因子和隶属度函数的模糊粒子群优化算法(CFPSO),对运输路线和第三方代理商选择进行决策。仿真实验中设计了3个不同规模的算例,并将收敛模糊粒子群优化算法的实验结果与枚举算法、基本粒子群优化算法、遗传算法和量子粒子群优化算法的实验进行对比分析,证明了模型和算法的有效性。     

基于混沌扰动和邻域交换的蚁群算法求解车辆路径问题

为求解车辆路径问题,提出一种新的基于混沌扰动和邻域交换的蚁群算法。针对标准蚁群算法存在搜索时间长,容易出现早熟收敛,得到的解不是最优解等缺点,新算法利用混沌的随机性、遍历性及规律性,在算法陷入早熟时,对小部分路径的信息素采用混沌扰动策略进行调整;针对标准蚁群算法的贪心规则随机性缺点,新算法采用邻域交换策略对最优解进行调整。在用于求解不同规模车辆路径问题的仿真结果表明,新算法比标准蚁群算法和遗传算法具有更好的效果。     

文化基因算法在多约束背包问题中的应用

文化基因算法是一种启发式算法,与一些经典数学方法相比,更适于求解多约束背包问题.文化基因算法是一种基于种群的全局搜索和基于个体的局部启发式搜索的结合体,针对多约束问题,提出采用贪婪策略通过违反度排序的方法处理多约束条件,全局搜索采用遗传算法,局部搜索采用模拟退火策略,解决具有多约束条件的0-1背包问题.通过对几个实例的求解,表明文化基因算法与标准遗传算法相比,具有更优的搜索性能.     

基于自适应免疫遗传算法的VRP问题的研究

物流配送车辆路径优化问题是近年来物流领域中的研究热点,该问题属于NP难题,当问题规模较大,很难得到问题的最优解和满意解。应用遗传算法是被认为求解NP难题的有效手段之一,文中在求解物流配送车辆路径优化问题时,在传统遗传算法的基础上,加入自适应算子,并引入了免疫算法的思想,实验结果表明该算法具有更好的全局和局部搜索能力和收敛速度,可有效地解决物流配送车辆路径优化问题。     

基于Memetic算法的带时间窗车辆路径问题研究*

提出一种模拟文化进化的Memetic算法求解带时间窗的车辆路径问题。设计了一种实数编码方案,将离散的问题转为连续优化问题。采用邻域搜索帮助具备一定学习能力的个体提高寻优速度;采用禁忌搜索帮助部分个体跳出局部最优点,增强全局寻优性能。实验结果表明,该算法可以更有效地求出优化解,是带时间窗车辆路径问题的一种有效求解算法。     

Balancing exploration and exploitation in memetic algorithms: A learning automata approach

One of the problems with traditional genetic algorithms (GAs) is premature convergence, which makes them incapable of finding good solutions to the problem. The memetic algorithm (MA) is an extension of the GA. It uses a local search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or reach solutions that would otherwise be unreachable by evolution or a local search method alone. In this paper, we introduce a new algorithm based on learning automata (LAs) and an MA, and we refer to it as LA‐MA. This algorithm is composed of 2 parts: a genetic section and a memetic section. Evolution is performed in the genetic section, and local search is performed in the memetic section. The basic idea of LA‐MA is to use LAs during the process of searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by local search. For this purpose, we present a criterion for the estimation of success of the local search at each generation. This criterion is used to calculate the probability of applying the local search to each chromosome. We show that in practice, the proposed probabilistic measure can be estimated reliably. On the basis of the relationship between the genetic section and the memetic section, 3 versions of LA‐MA are introduced. LLA‐MA behaves according to the Lamarckian learning model, BLA‐MA behaves according to the Baldwinian learning model, and HLA‐MA behaves according to both the Baldwinian and Lamarckian learning models. To evaluate the efficiency of these algorithms, they have been used to solve the graph isomorphism problem. The results of computer experimentations have shown that all the proposed algorithms outperform the existing algorithms in terms of quality of solution and rate of convergence.     

A memetic algorithm for extending wireless sensor network lifetime

Extending the lifetime during which a wireless sensor network (WSN) can cover all targets is a key issue in WSN applications such as surveillance. One effective method is to partition the collection of sensors into several covers, each of which must include all targets, and then to activate these covers one by one. Therefore, more covers enable longer lifetime. The problem of finding the maximum number of covers has been modeled as the SET K-COVER problem, which has been proven to be NP-complete. This study proposes a memetic algorithm to solve this problem. The memetic algorithm utilizes the Darwinian evolutionary scheme and Lamarckian local enhancement to search for optima given the considerations of global exploration and local exploitation. Additionally, the proposed algorithm does not require an upper bound or any assumption about the maximum number of covers. The simulation results on numerous problem instances confirm that the algorithm significantly outperforms several heuristic and evolutionary algorithms in terms of solution quality, which demonstrate the effectiveness of the proposed algorithm in extending WSN lifetime.     

A memetic algorithm for the generalized traveling salesman problem

The generalized traveling salesman problem (GTSP) is an extension of the well-known traveling salesman problem. In GTSP, we are given a partition of cities into groups and we are required to find a minimum length tour that includes exactly one city from each group. The recent studies on this subject consider different variations of a memetic algorithm approach to the GTSP. The aim of this paper is to present a new memetic algorithm for GTSP with a powerful local search procedure. The experiments show that the proposed algorithm clearly outperforms all of the known heuristics with respect to both solution quality and running time. While the other memetic algorithms were designed only for the symmetric GTSP, our algorithm can solve both symmetric and asymmetric instances.     

A memetic algorithm for the re-entrant permutation flowshop scheduling problem to minimize the makespan

A common assumption in the classical permutation flowshop scheduling model is that each job is processed on each machine at most once. However, this assumption does not hold for a re-entrant flowshop in which a job may be operated by one or more machines many times. Given that the re-entrant permutation flowshop scheduling problem to minimize the makespan is very complex, we adopt the CPLEX solver and develop a memetic algorithm (MA) to tackle the problem. We conduct computational experiments to test the effectiveness of the proposed algorithm and compare it with two existing heuristics. The results show that CPLEX can solve mid-size problem instances in a reasonable computing time, and the proposed MA is effective in treating the problem and outperforms the two existing heuristics.     

Memetic algorithm using multi-surrogates for computationally expensive optimization problems

In this paper, we present a multi-surrogates assisted memetic algorithm for solving optimization problems with computationally expensive fitness functions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogate in the spirit of Lamarckian learning. Inspired by the notion of ‘blessing and curse of uncertainty’ in approximation models, we combine regression and exact interpolating surrogate models in the evolutionary search. Empirical results are presented for a series of commonly used benchmark problems to demonstrate that the proposed framework converges to good solution quality more efficiently than the standard genetic algorithm, memetic algorithm and surrogate-assisted memetic algorithms.     

带时间窗车辆路径问题的文化基因算法

针对物流配送中带时间窗的车辆路径问题（Vehicle Routing Problem with Time Windows,VRPTW）,建立了数学模型,并设计了求解VRPTW的文化基因算法。种群搜索采用遗传算法的进化模式,局部搜索采用禁忌搜索机制,并结合可行邻域结构避免对不可行解的搜索,以提高搜索效率。与单纯的遗传算法和禁忌搜索算法进行对比实验,表明该算法是求解VRPTW的一种有效方法。     

基于文化基因算法和最小二乘支持向量机的安全数据特征处理方法

随着各类生物智能演化算法的日益成熟,基于演化技术及其混合算法的特征选择方法不断涌现。针对高维小样本安全数据的特征选择问题,将文化基因算法(Memetic Algorithm,MA)与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)进行结合,设计了一种封装式(Wrapper)特征选择方法(MA-LSSVM)。该方法利用最小二乘支持向量机易于求解的特点来构造分类器,以分类的准确率作为文化基因算法寻优过程中适应度函数的主要成分。实验表明,MA-LSSVM可以较高效地、稳定地获取对分类贡献较大的特征,降低了数据维度,提高了分类效率。