基于信息熵和混沌理论的遗传—蚁群协同优化算法

为了融合遗传算法和蚁群算法在解决组合优化问题方面的优势,提出一种基于信息熵和混沌理论的遗传.蚁群协同优化算法.利用信息熵产生初始群体,增加初始群体的多样性,并将混沌优化的遍历特性引入融合的遗传.蚁群算法,改进相关参数,实现参数的自适应控制以及遗传算法与蚁群算法混合优化策略的有机集成.通过仿真实例表明了混合智能算法在解决...     

A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem.A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.     

基于新颖蚁群算法的加工中心组成问题研究

针对蚁群算法求解加工中心组成问题易陷入早熟收敛状态的缺点,提出了将听觉信号、记忆矩阵与蚁群算法相融合的一种新颖蚁群算法。在仿真实验中,分别采用蚁群算法、加入听觉信号的蚁群算法、加入记忆矩阵的蚁群算法和新颖蚁群算法对加工中心组成问题进行求解。实验结果表明,新颖蚁群算法能够有效提高蚁群算法的全局寻优能力,收敛速度快,且所求得的组功效优于以上三个策略及以往的混合遗传算法。     

混合蚁群算法及其用于有机物毒性的QSAR研究

设计一种新的混合蚁群算法.该算法以一种新的二进制蚁群算法为基础,混合PBIL(population based incremental learning)算法及遗传算法的交叉操作和变异操作,从而大大提高了种群的多样性及收敛速度,改善全局最优解的搜索能力.通过函数优化测试,表明该算法具有良好的收敛速度和稳定性,最后用于有机物毒性的QSAR研究中,取得较好效果.     

混合行为蚁群算法在快递路径优化中的应用

将蚁群算法应用于快递路径选择上,并提出了混合行为蚁群算法,仿真结果表明该算法能有效提高收敛速度。     

GACO – A HYBRID ANT COLONY OPTIMIZATION METAHEURISTIC FOR THE DYNAMIC LOAD-BALANCED CLUSTERING PROBLEM IN AD HOC NETWORKS

We present the design of a novel hybrid genetic ant colony optimization (GACO) metaheuristic. Genetic ant colony optimization is designed to address the dynamic load-balanced clustering problem formulated from ad hoc networks. The main algorithm in GACO is ACO. Whenever an environment change occurs (i.e., nodes move), it makes use of a genetic algorithm to quickly achieve adaptation by refocusing the search process around promising areas of the search space induced by the new problem structure. Compared to other ACO approaches for dynamic problems, GACO does not depend on any problem-specific heuristics to repair or deconstruct solutions. Genetic ant colony optimization also does not require the knowledge of the specific changes that occurred. We compare GACO with three other adaptation methods, namely, P-ACO, PAdapt, and GreedyAnts. P-ACO is a population-based ACO approach that invokes a repair algorithm on its population of solutions when an environment change occurs. PAdapt works by adapting the values of major ACO parameters, while GreedyAnts employs a group of ants that construct solutions in a greedy manner. Empirical results show that GACO is able to react and recover faster from any performance degradation. Genetic ant colony optimization also produces better solutions within the allowable recovery window. These results are shown to be statistically significant.     

遗传-蚁群算法在智能交通中的应用

随着私家车的增多,城市交通问题越来越严重。为了解决这个问题,人们将计算机技术运用于城市智能交通系统(intelligent transportation systems,ITS)中。行车路径规划是城市智能交通体系中重要的一个环节。目前,有不少路径优化算法被提出用于解决行车路径规划问题,但各有不足。因此,提出了一种混合遗传蚁群算法(GACHA)。从基本蚁群算法入手,结合遗传和蚁群算法的各自优点,将两种算法的寻优过程循环多次结合。在蚁群算法的一次迭代循环后,将蚁群算法产生的较优解代替遗传算法中的部分个体,用以加快遗传算法的迭代速度。同时,将遗传算法算出的解设为较优路径来更新蚁群算法中的信息素分配,实现参数调整。多次相互指导能有效解决蚁群算法前期效率低和遗传算法后期冗余迭代的问题。实验结果表明,遗传-蚁群混合算法可以有效地避免陷入局部最优解,提高计算效率。它具有良好的优化和收敛性,能够准确地找到满足路网综合要求的最优路径。     

求解旅行商问题的混合量子蚁群算法

针对蚁群算法求解旅行商问题时易陷入局部最优和收敛速度慢的问题,提出一种新的求解旅行商问题的混合量子蚁群算法。该算法采用量子比特的概率幅对各路径上的信息素进行编码,采用量子旋转门及蚂蚁走过的路径对信息素进行更新,设计一种新的变换邻域准则。基于TSPLIB的仿真实验结果表明了该算法具有较快的收敛速度和求解精度。     

免疫遗传蚁群融合算法

提出了一种融合蚁群系统、免疫算法和遗传算法的混合算法。将免疫算法和遗传算法引入到每次蚁群迭代的过程中,利用免疫算法的局部优化能力和遗传算法的全局搜索能力,来提高蚁群系统的收敛速度。该算法通过遗传算法的选择、交叉、变异操作和免疫算法的自适应疫苗接种操作,有效地解决了蚁群系统的易陷入局部最优和易退化的缺点。通过对旅行商问题的仿真实验表明该算法具有非常好的收敛速度和全局最优解的搜索能力。     

Hybridizing ant colony optimization via genetic algorithm for mixed-model assembly line balancing problem with sequence dependent setup times between tasks

This paper presents a new hybrid algorithm, which executes ant colony optimization in combination with genetic algorithm (ACO-GA), for type I mixed-model assembly line balancing problem (MMALBP-I) with some particular features of real world problems such as parallel workstations, zoning constraints and sequence dependent setup times between tasks. The proposed ACO-GA algorithm aims at enhancing the performance of ant colony optimization by incorporating genetic algorithm as a local search strategy for MMALBP-I with setups. In the proposed hybrid algorithm ACO is conducted to provide diversification, while GA is conducted to provide intensification. The proposed algorithm is tested on 20 representatives MMALBP-I extended by adding low, medium and high variability of setup times. The results are compared with pure ACO pure GA and hGA in terms of solution quality and computational times. Computational results indicate that the proposed ACO-GA algorithm has superior performance.     

求解包含复杂关联约束的JSSP 的二级嵌套混合算法

作业车间调度问题(JSSP)包含“设备分配”和“工序排序” 2 个相互耦合的子问题,目 前的研究主要集中于工序串行的小规模问题。如果工序之间还存在并行、甚至嵌套等复杂关联 约束,则可行域性状非常复杂,当规模较大时,甚至难以求得可行解。针对以上难点问题,在 分别发挥遗传算法求解“分配问题”和蚁群算法求解“排序问题”的优势基础上,提出了二级嵌套 模型及其基本思路。通过一系列改进策略,如：基于工序的整数编码策略、基于设备类型的多 节点交叉策略、设备类别区间内基因互换的变异策略、基于逆向遍历的可行路径形成策略、基 于最短加工时间的信息素播洒与更新策略等等,构造了集成遗传算法与蚁群算法于同一循环体 的二级嵌套混合算法。针对中等规模问题,分别采用遗传算法、蚁群算法、二级嵌套蚁群算法、 遗传算法与蚁群算法相结合的二级嵌套混合算法,进行了对比试验研究。结果验证了所提算法 的可靠性和优越性,为求解包含复杂关联约束的JSSP 提供了新思路和新方法。     

基于二次分配问题的混合蚁群算法

二次分配问题是组合优化领域中经典的NP-hard问题之一,应用广泛。在对二次分配问题进行分析的基础上,提出了一种求解该问题的混合蚁群算法。该算法通过在蚁群算法中引入遗传算法的2-交换变异算子,增强了算法的局部搜索能力,提高了解的质量。实验结果表明,该算法在求解二次分配问题时优于蚁群算法和遗传算法。     

基于混合蚁群优化的边缘计算细粒度任务调度方法

为缓解中心服务器的压力,制定合理的调度方案,基于混合蚁群优化算法提出了边缘计算细粒度任务调度方法。描述边缘计算任务调度问题,并设置假设条件,简化调度求解难度。通过计算任务的优先指数,按照从大到小的顺序排列后组成任务队列。分析边缘服务器性能特征,明确边缘服务器处理能力。构建能耗以及时延多目标函数,并设置约束条件,利用混合蚁群优化算法求解多目标函数,完成边缘计算细粒度任务调度方案设计。结果表明：该方法应用下的任务调度能耗和时延更小,说明所提方法性能更优,所获得的调度方案更合理。     

Hybrid large neighbourhood search algorithm for capacitated vehicle routing problem

This paper presents a new hybrid algorithm that executes large neighbourhood search algorithm in combination with the solution construction mechanism of the ant colony optimization algorithm (LNS–ACO) for the capacitated vehicle routing problem (CVRP). The proposed hybrid LNS–ACO algorithm aims at enhancing the performance of the large neighbourhood search algorithm by providing a satisfactory level of diversification via the solution construction mechanism of the ant colony optimization algorithm. Therefore, LNS–ACO algorithm combines its solution improvement mechanism with a solution construction mechanism. The performance of the proposed algorithm is tested on a set of CVRP instances. The hybrid LNS–ACO algorithm is compared against two other LNS variants and some of the formerly developed methods in terms of solution quality. Computational results indicate that the proposed hybrid LNS–ACO algorithm has a satisfactory performance in solving CVRP instances.     

A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations

This paper presents a novel hybrid optimization approach based on differential evolution algorithm and receptor editing property of immune system. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing industry. The proposed hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of hybrid particle swarm algorithm, ant colony algorithm, immune algorithm, hybrid immune algorithm, genetic algorithm, feasible direction method and handbook recommendation.     

基于混合蚁群优化的卫星地面站系统任务调度方法

卫星地面站系统任务调度是一个典型的组合优化问题, 优化过程极其复杂. 鉴于此, 提出了一种有效求解该问题的基于蚁群优化算法和导向局部搜索方法的混合优化方法. 该方法将蚁群优化和导向局部搜索有效地结合在一起, 极大地提高了优化绩效. 实例计算结果表明, 该混合方法能有效地求解卫星地面站系统任务调度问题.     

蚁群优化算法求解TSP问题研究

介绍了信息素混合更新的蚁群优化算法,并用来求解TSP问题。混合信息素更新的蚁群优化算法是在蚁群系统（ACS）的基础上改进而成的,它在演化过程中,通过改变信息素的迭代最优更新规则和全局最优更新规则的使用频率,逐渐增加全局最优更新规则的使用频率,从而提高系统收敛的速度和减少系统搜索的导向性,并以Oliver30和att48为例给出了实验结果,说明了该混合算法的有效性。     

求解混杂生产调度问题的嵌套混合蚁群算法

蚁群算法作为解决优化问题的有力工具,它的有效性已经得到了证明.由于其生物学背 景,基本蚁群算法被设计来求解复杂的排序类型组合优化问题,在连续空间优化问题的求解方面 研究很少.本文提出一种嵌套混合蚁群算法,用于解决具有混杂变量类型的复杂生产调度问题, 在一种新的最佳路径信息素更新算法的基础上,提高了搜索效率.计算机仿真结果表明,本文提 出的方法在求解此类问题上性能优于另一种基于进化计算的有效方法--遗传算法.     

Subdomain generation using emergent ant colony optimization

Finite elements mesh decomposition is a well known optimization problem and is used to split a computationally expensive finite elements mesh into smaller subdomains for parallel finite elements analysis.The ant colony optimization is a type of algorithm that seeks to model the emergent behaviour observed in ant colonies and utilize this behaviour to solve combinatorial problems. This technique has been applied to several problems, most of which are graph related because the ant colony metaphor can be most easily applied to such types of problems. This paper examines the application of ant colony optimization algorithm to the partitioning of unstructured adaptive meshes for parallel explicit time-stepping finite elements analysis.The concept of ant colony optimization technique in addition to the notion of swarm intelligence for finding approximate solutions to combinatorial optimization problems is described. This algorithm combines the features of the classical ant colony optimization technique with swarm intelligence to form a model which is an artificial system designed to perform a certain task.The application of the ant colony optimization for partitioning finite elements meshes based on triangular elements using the swarm intelligence concept is described. A recursive greedy algorithm optimization method is also presented as a local optimization technique to improve the quality of the solutions given by the ant colony optimization algorithm. The partitioning is based on the recursive bisection approach.The mesh partitioning is carried out using normal and predictive modes for which the predictive mode uses a trained multi-layered feedforward neural network that estimates the number of triangular elements that will be generated after finite elements mesh generation is carried out.The performance of the proposed hybrid approach for the recursive bisection of finite elements meshes is examined by decomposing two mesh examples and comparing them with a well known finite elements domain decomposer.     

Ant colony optimization combined with taboo search for the job shop scheduling problem

In this paper, we present a hybrid algorithm combining ant colony optimization algorithm with the taboo search algorithm for the classical job shop scheduling problem. Instead of using the conventional construction approach to construct feasible schedules, the proposed ant colony optimization algorithm employs a novel decomposition method inspired by the shifting bottleneck procedure, and a mechanism of occasional reoptimizations of partial schedules. Besides, a taboo search algorithm is embedded to improve the solution quality. We run the proposed algorithm on 101 benchmark instances and obtain competitive results and a new best upper bound for one open benchmark instance is found.