基于遗传-蚁群算法的无线Mesh网QoS路由算法研究

针对无线Mesh网QoS的路由特点,结合遗传算法扣蚁群算法的特性,设计了一种遗传算法和蚁群算法相融合的算法,提出了遗传-蚁群算法求解无线Mesh网QoS路由问题的解决方案.该算法采用遗传算法生成初始信息素分布,利用蚁群算法求精确解,并在遗传算法运行过程中动态确定遗传算法与蚁群算法的最佳融合时机,实现两个算法的优势互补.实验结果表明,该算法在无线Mesh网QoS路由选择中是高效的,性能明显优于遗传算法和蚁群算法.     

蚁群算法在武器目标分配问题中的应用

武器-目标分配问题(Weapon-Target Assignment Problem)是一种典型的NP问题.在分析多种算法的和建立武器目标分配的优化模型基础上.本文提出求解武器目标分配问题的一种改进蚁群算法,加快收敛速度的同时保证了解的有效性.     

基于免疫记忆的蚁群算法的WTA问题求解

武器-目标分配(WTA)是影响武器系统作战有效性的重要因素之一。该文在蚁群算法中增加一个额外的记忆库,利用免疫记忆和克隆选择的思想和方法,提出了基于免疫记忆的蚁群算法(IMBACA),并用于求解武器-目标分配问题。分别用给定数据集和随机数据集的WTA问题进行实验,并与传统蚁群算法和蚁群算法的混合算法进行比较,结果显示IMBACA在解的质量和时间性能上均取得了较好的 效果。     

多约束条件下路由选择算法研究

采用遗传算法和蚁群算法相结合的融合算法，继承了遗传算法和蚁群算法的优点，在求精解效率上优于遗传算法，在时间效率上优于蚁群算法，能很好地实现多约束条件的最优路径计算，测试证明融合算法优化性能和时间性能都取得了很好的效果。     

遗传算法结合蚁群算法电力系统故障浅析

根据蚁群算法与遗传算法的特性,提出了与遗传算法混合的蚁群算法（G3A）：由遗传算法生成初始信息素分布,在蚁群算法寻优中,利用蚁群算法信息素轨迹更新求精确解,保持了遗传算法的全面搜索能力,从而获得在时间效率与精解效率都更优的一种新的启发式方法。     

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。     

粒子群优化在嵌入式软硬件划分中的应用

针对嵌入式系统设计中的软硬件划分问题,提出了一种基于粒子群优化(PSO)算法的划分策略,并将该算法与整数线性规划、遗传算法、蚁群算法等进行计算机仿真比较。结果表明,该方法获得的最优解优于遗传算法和蚁群算法两种元启发式算法,充分接近由整数线性规划得到的最优解;在算法执行时间方面,该方法也优于其它三种算法。     

WTA问题的遗传算法研究

提出一种解武器－目标分配问题（weapon target assignment,WTA)的遗传算法,此方法根据遗传算法理论,设计了一种新的武器－目标分级在系式,并缩小了搜索的可行解空间,经多个战例的仿真表明此算法不仅全局收敛性好,稳定性高,易于进行并行处理,而且每个解都具有实际的可分配性。     

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。     

协同多目标攻击的混合蛙跳融合蚁群算法研究

针对未来超视距条件下的多机协同空战,提出了一种基于混合蛙跳融合蚁群算法的目标分配方法。以目标威胁评估值为准则建立空战决策模型,根据空战决策特点对青蛙粒子进行特殊编码处理,在混合蛙跳算法局部搜索过程中加入自适应差分扰动机制、在蚁群算法中引入变异算子以减少算法搜索时间。融合算法利用混合蛙跳算法快速的全局搜索能力生成初始优化解群,利用蚁群算法具有正反馈的特点求精确解,利用Matlab仿真。仿真结果表明该方法能够快速有效地给出合理的目标分配方案。     

初始信息素筛选的蚁群优化算法在HDFS副本选择中的研究

随着社会信息化程度的不断提高,各种形式的数据急剧膨胀.HDFS成为解决海量数据存储问题的一个分布式文件系统,而副本技术是云存储系统的关键.提出了一种基于初始信息素筛选的蚁群优化算法(InitPh_ACO)的副本选择策略,通过将遗传算法(GA)与蚁群优化算法(ACO)算法相结合,将它们进行动态衔接.提出基于初始信息素筛选的ACO算法,既克服了ACO算法初始搜索速度慢,又充分利用GA的快速随机全局搜索能力.利用云计算仿真工具CloudSim来验证此策略的效果,结果表明:InitPh_ACO策略在作业执行时间、副本读取响应时间和副本负载均衡性三个方面的性能均优于基于ACO算法的副本选择策略和基于GA的副本选择策略.     

一种基于新型优化算法的多播路由选择方法

遗传算法、蚁群优化算法已在多播路由优化问题中得到了广泛应用,但由于算法本身的缺陷,二者在具体应用时都存在着时间性能与优化性能之间的矛盾。论文将遗传算法与蚁群优化算法二者合成,优势互补。仿真实验表明,应用这种算法于多播路由问题,可以得到比现有启发式算法更好的结果。     

On the design of hybrid bio-inspired meta-heuristics for complex multiattribute vehicle routing problems

This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta-heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as main approach and GA as local search. GA-ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO-GA approach outperforms the others. Considering the cost, ACO, GA, and GA-ACO provide better results. Regarding computation time, GA and GA-ACO have been found the most competitive among the benchmark.     

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.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

An auto controlled ant colony optimization algorithm controls the behavior of the ant colony algorithm automatically based on a priori heuristic. During the experimental study of auto controlled ACO algorithm on grid scheduling problem, it was observed that the induction of lazy ants not only reduces the time complexity of the algorithm but also produces better results on the given objectives. Lazy ants are basically a mutated version of active ants that remain alive till the fitter lazy ants are generated in the successive generations. This work presents an improved auto controlled ACO algorithm using the lazy ant concept. Performance study reveals the efficacy and the efficiency achieved by the proposed algorithm. A comparative study of the proposed method with some other recent meta-heuristics such as auto controlled ant colony optimization algorithm, genetic algorithm, quantum genetic algorithm, simulated annealing and particle swarm optimization for grid scheduling problem exhibits so.     

基于遗传算法与蚁群算法动态融合的网格任务调度

深入分析遗传算法和蚁群算法的机理,并结合网格任务调度的研究,提出基于遗传算法和蚁群算法动态融合的网格任务调度策略.该策略通过不同迭代次数中种群相似度的差值实现两种算法的动态融合.仿真实验表明该策略是可行的,并且具有高效性.     

基于自适应蚁群优化算法的认知决策引擎

认知决策引擎的设计是认知无线电系统中的一项关键技术,它的主要功能是依据通信环境的变化和用户需求动态地配置无线电工作参数。提出了一种基于自适应蚁群算法的认知决策引擎来实现工作参数的最优化配置。该算法在基本蚁群算法的基础上加入了路径选择机制和信息素挥发因子自适应调整机制,保证了算法的全局搜索能力和收敛速度,有效地避免了容易陷入局部最优解的缺陷。仿真结果表明,在不同的环境下基于该算法的认知引擎比GA和ACO算法具有更好的性能。     

Ant colony optimization algorithm for the Euclidean location-allocation problem with unknown number of facilities

This paper addresses the Euclidean location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. This is a NP-hard problem and in this paper, a three-stage ant colony optimization (ACO) algorithm is introduced and its performance is evaluated by comparing its solutions to the solutions of genetic algorithms (GA). The results show that ACO outperformed GA and reached better solutions in a faster computational time. Furthermore, ACO was tested on the relaxed version of the problem where the number of facilities is known, and compared to existing methods in the literature. The results again confirmed the superiority of the proposed algorithm.     

多需求点车辆调度模型及其群体智能混合求解

建立货运关系明细的多需求点车辆调度模型, 模型求解过程是先由粒子群算法的粒子位置向量得到单车运送的货物, 再由蚁群算法优化单车路径, 根据优化目标筛选粒子, 直到终止条件, 实现所有货物对所有车辆的分配. 实例求解结果表明混合求解得到的车辆总路径小于蚁群算法得到的结果.