求解多目标优化问题的改进蚁群算法

蚁群算法是一种模拟蚂蚁行为进行优化的启发式优化算法,该算法在许多领域已经得到应用.针对多目标优化问题优化与求解较困难的问题,提出一种嵌入变尺度算法的改进蚁群算法用于求解,为蚁群算法在连续空间中的应用提供了怂一个可行的方案.给出了该算法的详细定义及实现步骤,实例仿真表明,该算法能加快收敛速率,对连续空间的蚁群算法研究具有重要的意义.     

一种求解函数优化的混合蚁群算法*

将遗传算法与蚁群算法中的协同模型进行有机结合,在蚁群算法中引入交叉、变异、选择算子来改进基本蚁群算法,克服了蚁群算法不太适合求解连续空间优化问题的缺陷。通过测试函数表明该方法具有较好的收敛速度和稳定性,求解结果好于遗传算法。     

蚁群算法在系统辨识中的应用

将传统用于离散空间问题求解的蚁群算法引入连续空间内的系统参数辨识问题求解, 定义了各智能单蚁的信息量分布函数和相应的系统辨识求解算法,并在线性系统参数辨识的实 例仿真中得到了很好的结果,显示了蚁群算法在连续空间优化问题中的应用前景.最后,对蚁群 算法在连续空间优化领域中的适用特征作了总结,并指出了今后进一步工作的方向.     

一种连续空间优化问题的蚁群算法及应用

针对随机优化算法收敛困难及搜索时间较长的问题,提出一种求解连续空间优化问题的蚁群算法,为蚁群算法在连续空间中的应用提供了一个可行的方案。给出了该算法的详细定义及实现步骤,并将该算法应用于多变量函数优化及热工控制系统控制器参数优化,仿真结果表明:该算法具有良好的全局优化性能,能加快收敛速率,解决了随机优化算法收敛困难的问题,并提高寻优精度。     

变尺度混沌蚁群优化算法

将变尺度混沌搜索算法融合到蚁群算法中,并用于求解连续空间优化问题。蚁群算法每一次迭代结束时,就使用混沌搜索算子在当前全局最优解附近搜索更好的解。而随着蚁群算法的进行,混沌算子搜索范围逐渐缩小,这样,混沌算子在蚁群搜索的初期起到防止陷入局部最优的作用,在蚁群搜索后期起到提高搜索精度的作用。将变尺度混沌蚁群优化算法用于求解函数优化问题的实验结果表明,该算法在求解包括欺骗性函数和高维函数在内的多种测试函数优化问题方面具有很好的效果。     

求解TSP的带混沌扰动的模拟退火蚁群算法

针对蚁群算法易陷入局部最优及收敛速度较慢的问题,提出一种带混沌扰动的模拟退火蚁群算法。引入模拟退火机制及混沌系统,分别对基本蚁群算法中的蚂蚁种群搜寻范围以及信息素设定与更新进行改进,提高蚁群算法全局搜索能力。使用该算法与基本蚁群算法同时求解TSP这一经典组合优化问题,对两种算法的求解性能进行对比分析。仿真结果表明,该算法的求解精度及求解效率都明显优于基本蚁群算法。     

蚁群算法求解连续空间优化问题的一种方法

针对蚁群算法不太适合求解连续性优化问题的缺陷,提出用蚁群算法求解连续空间优化问题的一种方法.该方法将解空间划分成若干子域,在蚁群算法的每一次迭代中,首先根据信息量求出解所在的子域,然后在该子域内已有的解中确定解的具体值.以非线性规划问题为例所进行的计算结果表明,该方法比使用模拟退火算法、遗传算法具有更好的收敛速度.     

双态免疫优势蚁群算法及其在TSP中的应用研究

通过分析标准蚁群算法易于出现早熟停滞现象,该文提出一种高效收敛的算法-双态免疫优势蚁群算法.该算法将蚂蚁分成两种状态,扩大了解的搜索空间,有效抑制了收敛过程中的早熟停滞现象,将禁忌表中的抗体通过克隆扩增、高频变异等免疫算子操作得到精英蚂蚁,再对抗体记忆库引入局部最优免疫策略.针对TSP实验结果表明:该算法与最新的改进蚁群优化算法相比,其收敛速度及求解精度均得到了提高.     

基于蚁群算法的PID参数寻优

蚁群算法是一种新型的模拟进化算法,该算法用于离散空间问题的求解取得了较好的结果.该文将蚁群算法引入连续空间,研究了基于蚁群算法的PID参数优化问题,给出了仿真实例,结果表明蚁群算法用于解决连续空间优化问题是可行且有效的.蚁群算法具有较好的鲁棒性,它采用分布式计算,具有本质并行性.     

传感器网络中单源单汇路由问题的模型与算法

路由问题是无线传感器网络中的核心问题之一,寻找从源到汇的最小费用路径非常困难。蚁群优化算法是最近提出的求解复杂组合优化问题的启发式算法,该算法能够在完全分布式环境下对复杂问题进行求解。文章建立了无线传感器网络中单源单汇路由问题的数学模型,并给出了基于蚁群优化的求解算法。     

基于文化的连续蚂蚁优化算法的研究*

针对蚂蚁优化算法在求解连续空间问题方面的缺陷,提出一种基于文化的连续蚂蚁优化算法。该算法将蚂蚁优化算法纳入文化算法的框架,组成基于蚂蚁优化算法的主群体和信念的两大空间。在知识和群体层面使用双重进化机制支持问题的求解和知识的提取,从而充分利用精英蚂蚁所携带的特征信息,在很大程度上提高了收敛速度,增强了搜索的多样性。实验结果表明,该算法求解速度快、寻优成功率高,是一种提高蚂蚁优化算法性能的有效算法。     

SoC测试访问机制和测试壳的蚁群联合优化

针对系统级芯片(SoC)测试壳优化和测试访问机制的测试总线划分问题,提出了基于蚁群算法的SoC Wrapper/TAM联合优化方法.构造蚁群算法时首先进行IP核的测试壳优化,用于缩短最长扫描链长度,减少单个IP核的测试时间;在此基础上进行TAM结构的蚁群优化,通过算法迭代逼近测试总线的最优划分,从而缩短SoC测试时间.对ITC2002基准SoC电路进行实验的结果表明,该方法能有效地解决SoC测试优化问题.     

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance.     

求解多处理机调度问题的蚁群算法

蚁群算法是受自然界中的蚂蚁觅食行为启发而设计的智能优化算法，特别适合处理离散型的组合优化问题。提出一种求解多处理机调度的蚁群算法,利用一个蚂蚁代表一个处理机来选择任务，并通过分析关键路径及每个任务的最早、最迟开始时间来确定每个任务的紧迫程度，让蚂蚁以此来选择任务。实验证明，该算法可比传统算法取得有更好运行效率的调度策略。     

A novel hybrid algorithm for scheduling steel-making continuous casting production

In this paper, steel-making continuous casting (SCC) scheduling problem (SCCSP) is investigated. This problem is a specific case of hybrid flow shop scheduling problem accompanied by technological constraints of steel-making. Since classic optimization methods fail to obtain an optimal solution for this problem over a suitable time, a novel iterative algorithm is developed. The proposed algorithm, named HANO, is based on a combination of ant colony optimization (ACO) and non-linear optimization methods.     

High speed ant colony optimization CMOS chip

Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies’ behavior. This paper presents design and CMOS implementation of the ant colony optimization based algorithm for solving the TSP problem. In order to implement ant colony optimization algorithm in CMOS, we will present a new algorithm. This algorithm is based on the original ant colony optimization but it can be implemented in CMOS. Briefly, pheromone matrix is transformed on the chip area and ants move up-down through the pheromone matrix and they make their decisions. Finally ants select a global path. In previous researches only pheromone values is used, but select the next city in this paper is based on heuristics value and pheromone value. In definition of problem, we use heuristics value as a matrix. Previous researches could not be used for wide type of optimization problem but our chip gives heuristics value initially and we can change initial value of heuristics value according to the optimization problem so this capability increases the flexibility of ACO chip. Simple circuit is used in blocks of our chip to increase the speed of convergence of ACO chip. We use Linear Feedback Shift Register (LSFR) circuit for random number generator in ACO chip. ACO chip has capability of solving the big TSP problem. ACO chip is simulated by HSPICE software and simulation results show the good performance of final chip.     

时间依赖型车辆路径问题的一种改进蚁群算法

时间依赖型车辆路径规划问题(TDVRP),是研究路段行程时间随出发时刻变化的路网环境下的车辆路径优化.传统车辆路径问题(VRP)已被证明是NP-hard问题,因此,考虑交通状况时变特征的TDVRP问题求解更为困难.本文设计了一种TDVRP问题的改进蚁群算法,采用基于最小成本的最邻近法(NNC算法)生成蚁群算法的初始可行解,通过局部搜索操作提高可行解的质量,采用最大--最小蚂蚁系统信息素更新策略.测试结果表明,与最邻近算法和遗传算法相比,改进蚁群算法具有更高的效率,能够得到更优的结果;对于大规模TDVRP问题,改进蚁群算法也表现出良好的性能,即使客户节点数量达到1000,算法的优化时间依然在可接受的范围内.     

一类用于连续域寻优的蚁群算法

由真实蚁群觅食行为启发而来的经典蚁群算法,非常适合解决组合优化问题,但经典蚁群算法的离散性本质也限制了其在连续空间问题求解中的应用。为此,提出了一种用于连续域寻优的改进蚁群算法。局部搜索上基于解决离散域问题的经典蚁群优化思想,全局搜索利用类似于遗传算法的交叉、变异操作-称为Ant Diffusion和Ant Walk方法,每代寻优结束后均采用"精英策略"把本代最优个体保留到下一代中。最后,采用改进算法对几个基准函数做了寻优测试,都取得了良好的效果,证明了算法的有效性。     

基于粒子群优化的蚁群算法在TSP中的应用

结合粒子群算法的问题,提出用混合蚁群算法来求解著名的旅行商问题.问题的核心是应用粒子群算法对蚁群算法的控制参数:启发式因子、信息素挥发系数、随机性选择阈值进行优化,以及运用蚁群系统算法寻找最短路径.新算法对于蚂蚁算法中的参数调整大大减低,减少了大量盲目的实验,力求在开发最优解和探究搜索空间上找到平衡点.对旅行商问题的仿真实验表明,新算法的优化质量和效率都优于传统蚁群算法和遗传算法,接近理论最佳值.新算法也可推广用于其他NP问题的求解.     

Structural topology optimization using ant colony optimization algorithm

The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described. A discretized topology design representation and the method for mapping ant's trail into this representation are then detailed. Subsequently, a modified ACO algorithm with elitist ants, niche strategy and memory of multiple colonies is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of multi-modal optimal design.