Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed ＂continuous orthogonal ant colony＂ （COAC）, whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an ＂adaptive regional radius＂ method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.     

A Multi-pipe Path Planning by Modified Ant Colony Optimization

Path planning in 3D geometry space is used to find an optimal path in the restricted environment,according to a certain evaluation criteria.To solve the problem of long searching time and slow solving speed in 3D path planning,a modified ant colony optimization is proposed in this paper.Firstly,the grid method for environment modeling is adopted.Heuristic information is connected with the planning space.A semi-iterative global pheromone update mechanism is proposed.Secondly,the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number.Thirdly,co-evolutionary algorithm is used.Finally,the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.     

Corner block list representation and its application with boundary constraints

Floorplanning is a critical phase in physical design of VLSI circuits. The stochastic optimization method is widely used to handle this NP-hard problem. The key to the floorplanning algorithm based on stochastic optimization is to encode the floorplan structure properly. In this paper, corner block list (CBL)-a new efficient topological representation for non-slicing floorplan-is proposed with applications to VLSI floorplan. Given a corner block list, it takes only linear time to construct the floorplan. In floorplanning of typical VLSI design, some blocks are required to satisfy some constraints in the final packing. Boundary constraint is one kind of those constraints to pack some blocks along the pre-specified boundaries of the final chip so that the blocks are easier to be connected to certain I/O pads. We implement the boundary constraint algorithm for general floorplan by extending CBL. Our contribution is to find the necessary and sufficient characterization of the blocks along the boundary repre     

ACO-Steiner: Ant Colony Optimization Based Rectilinear Steiner Minimal Tree Algorithm

The rectilinear Steiner minimal tree （RSMT） problem is one of the fundamental problems in physical design, especially in routing, which is known to be NP-complete. This paper presents an algorithm, called ACO-Steiner, for RSMT construction based on ant colony optimization （ACO）. An RSMT is constructed with ants＇ movements in Hanan grid, and then the constraint of Hanan grid is broken to accelerate ants＇ movements to improve the performance of the algorithm. This algorithm has been implemented on a Sun workstation with Unix operating system and the results have been compared with the fastest exact RSMT algorithm, GeoSteiner 3.1 and a recent heuristic using batched greedy triple construction （BGTC）. Experimental results show that ACO-Steiner can get a short running time and keep the high performance. Furthermore, it is Mso found that the ACO-Steiner can be easily extended to be used to some other problems, such as rectilinear Steiner minimal tree avoiding obstacles, and congestion reduction in global routing.     

Research on customer-oriented product configuration technology of aircraft

Due to the defects of current aircraft product configuration technology,such as the gap between product design and customers’ requirements,the low configuration efficiency and etc.a customer-oriented product configuration solution of aircraft was proposed in this paper.Basing on a deep investigation into the market and the structure of the aircraft,this paper proposed a method of dividing and creating the aircraft options,in which we set up optional constraints to meet the demand of configuration and then optimized the constraint algorithm.This work can help to realize the optimum association between the aircraft options and modules.Basing on a research on customer demand,The paper established a customer demand model and a mapping relationship which exists between customer demand and aircraft options,It can also help to make a great contribution to build customer personalized demand of the aircraft configuration process and the aircraft configuration library.     

A Fuzzy Inference System for Increasing of Survivability and Efficiency in Wireless Sensor Networks

The nodes of a WSNs （wireless sensors network） are composed of small devices capable of sensing and transmitting data related to some phenomenon in the environment. These devices, named sensor nodes, have severe constraints, such as lower processing and storage capacity, and mainly they have severe constraints related to battery energy. Therefore, the developing of strategies to reduce the power consumption is one of the main challenges in WSNs, and thereby helping to increase the survivability and efficiency of these networks. This paper proposes a new approach to help multi-path routing protocols to choose the best route based on Fuzzy Inference Systems and ACO （ant colony optimization）. The Fuzzy System is used to estimate the degree of the route quality, based on the number of hops and the lowest energy level among the nodes that form the route. The ACO algorithm is used to adjust the rule base of the fuzzy system in order to improve the classification strategy of the route, and hence increasing the energy efficiency and the survivability of the network. The simulations showed that the proposal is effective from the point of view of the energy, the number of received messages, and the cost of received messages when compared against other approaches.     

Efficiency-aware and fairness-aware joint-layer optimization for downlink data scheduling in OFDM

Efficiency and fairness are two crucial issues to be considered for resource alloca- tion in multi-user wireless networks. Based on the joint optimization of physical layer and data link layer, an optimization model is derived to achieve efficient and fair downlink data scheduling in multi-user OFDM wireless networks by maximizing the total utility function with respect to the average waiting time of user queue. A dynamic sub-carrier allocation algorithm （DSAA） based on the optimization model is proposed in order to obtain the maximization of the total scheduling utility. Effi- ciency is improved by combining DSAA with time scale interference predictor （TSIP） which at large time scales predict ON/OFF period of user data with temporal corre- lation structure across multiple time scales in multi-user interference environment. Simulation results verify the efficiency and fairness of the scheme.     

The Structural Design,Simulation Analysis and Parameter Optimization of the Cheetah Robot’s Lumbar Vertebrae

The quality of skeleton system for the cheetah robot goes hand in hand with its bionic result of its shape, structure and functions. In view of the skeleton system constitution and structural characteristic of the cheetah, the team applied structure design, stimulation analysis and parameter optimization to developing the cheetah robot. In addition, after the invention of cheetah robot＇s anterior lumbar vertebra based on its functional attribute and connectivity attribute, the Solidworks Simulation was utilized to analyze the design, according to which improvement on the lumbar vertebra was made. Plus, the advantages of the CAD and CAE made the high efficiency of design work and high quality of the cheetah robot possible.     

Railway station route searching based on ACA

In the railway station signal system, the route searching algorithm is an important software module to realize the function safety of this system. In the paper, the ant colony algorithm is applied to the railway station route searching. Take the specific station route-the Wangzhai Station route for example to search the shortest route. The ideas of the Wangzhai Station route searching are put forward based on the ant colony algorithm. The implementation steps of it are presented. The control flow of it is proposed. The program is developed, the interface is worked out, and the route controlling is realized to demonstrate its good performance.     

Ant colony optimization for bearings-only maneuvering target tracking in sensors network

In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization （ACO）, then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter （IMMEKF） for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method. Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time.     

蚁群算法的收敛性分析及其在TSP上的求解

根据基本蚁群算法的特点对其收敛性进行分析,给出寻找最短路径的蚁群算法收敛的充分条件.并把算法运用到旅行商问题上,试验结果表明该算法在求解TSP问题上解的精度优于组合优化算法以及遗传算法且收敛速度比较快.     

基于人工免疫改进的搬运机器人蚁群路径规划

针对搬运机器人在障碍环境下的路径寻优问题,提出一种基于人工免疫改进的蚁群路径规划算法(AI-ACA)。蚁群算法(ACA)的规划依赖于信息素挥发系数、期望启发因子和信息启发因子等参数的选取,传统ACA通过经验来设定这三个参数,但路径寻优中的最优参数因障碍环境而异,为解决经验参数对不同环境路径寻优结果的影响,引入人工免疫算法(AIA),对ACA的相关参数进行迭代优化,以此改善路径寻优结果。仿真结果及在自制机器人平台上测试表明,AI-ACA对于不同障碍环境可以准确地进行路径规划,在同样环境下较所参考的定参数蚁群路径规划效果有明显提升,提高了整个系统的运输效率。     

基于细菌觅食的改进蚁群算法

蚁群算法是模仿蚂蚁觅食行为的一种新的仿生学智能优化算法。针对其收敛速度慢和易陷入局部最优的不足,将细菌觅食算法和蚁群算法相结合,提出一种细菌觅食 蚁群算法。在蚁群算法迭代过程中,引入细菌觅食算法的复制操作,以加快算法的收敛速度;引入细菌觅食算法的趋向操作,以增强算法的全局搜索能力。通过经典的旅行商问题和函数优化问题测试表明,细菌觅食 蚁群算法在寻优能力、可靠性、收敛效率和稳定性方面均优于基本蚁群算法及两种改进蚁群算法。     

混沌扰动模拟退火蚁群算法低碳物流路径优化

低碳物流是目前物流配送领域的热点研究课题,也是群体智能优化算法的重要应用方向。针对物流配送中碳排放的度量方法,以VRP问题为基本模型,以碳排放成本为目标函数,建立了低碳物流配送路径优化模型。为了避免基本蚁群算法出现停滞及早熟现象,提出了带混沌扰动的模拟退火蚁群算法来求解低碳物流配送路径优化模型。该算法将混沌系统及模拟退火机制引入基本蚁群算法,避免了算法陷入局部最优,增强了全局搜索能力,提高了求解效率。通过实验仿真及对比分析可知,带混沌扰动的模拟退火蚁群算法的求解结果明显优于基本蚁群算法,表明了该算法的有效性和合理性。     

基于群智能SOM算法的特征造型搜索技术

为解决语义特征化后复杂造型协同设计中造型共享库中的造型检索问题,提出了一种基于群智能自组织聚类算法。该算法首先将语义特征造型信息向量化,通过语义造型特征树得到语义特征造型特征集,以蚁群算法做为自组织准则,并以粒子群算法做为蚁群移动模型,将特征语义群分布在一个平面上进行聚类,递归收集聚类结果。试验证明,采用此种方法,可以对特征造型完成准确率很高的聚类,使特征库组织性得到了很大提高。     

改进蚁群算法的无线传感器网络路径优化

研究无线传感器网络路径优化问题,针对无线传感器网络(WSN)路径优化问题,在分析了遗传算法和蚁群算法各自优缺点的基础上,通过把蚁群算法作为WSN路径优化的主框架,采用遗传算的选择、交叉和变异算子提高蚁群算法搜索速度,提出一种改进蚁群算法的WSN路径优化方法。仿真结果表明,改进蚁群算法有效地克服了基本蚁群算法的缺陷,提高了WSN路径优化效率和成功率,减少了能理消耗,有效延长了网络生存时间。     

Improved ant colony optimization algorithm based on RNA computing

RNA computing is a new intelligent optimization algorithm, which combines computer science and molecular biology. Aiming at the weakness of slow convergence rate and poor global search ability in the basic ant colony optimization algorithm due to the unreasonable selection of parameters, this paper utilizes the combination of RNA computing and basic ant colony optimization algorithm to overcome the defects. An improved ant colony optimization algorithm based on RNA computing is proposed. In the iterative process of ant colony optimization algorithm, transformation operation, recombination operation and permutation operation in RNA computing are introduced to optimize the initial parameters including importance factor of pheromone trail α, importance factor of heuristic function β and pheromone evaporation rate ρ to improve the convergence efficiency and global search ability. The performance of the algorithm is evaluated on five instances of the library of traveling salesman problems (TSPLIB) and six typical test functions. The experimental results demonstrate that the proposed RNA-ant colony optimization algorithm is superior than basic ant colony optimization algorithm in optimization ability, reliability, convergence efficiency, stability and robustness.     

多目标优化的多种群混合行为二元蚁群算法

针对二元蚁群算法在求解多目标问题时难以同时得到多个解和难以得到Pareto曲面的缺陷,使用多种群策略,改善算法的全局搜索能力,引入环境评价/奖励因子和蚁群混合行为搜索机制,提出了多种群混合行为二元蚁群算法。通过对几个不同带约束多目标函数的测试,实验结果表明该算法在保证全局搜索能力的基础上,拥有很好的多目标求解能力。     

软件定义网络中蚁群优化的负载均衡算法

软件定义网络因其特定的网络结构，有集中控制获取与分配全球网络资源等特点。针对软件定义网络中的负载均衡问题，在原有蚁群算法的基础上，提出了一种改进的蚁群优化负载均衡算法，主要思想如下：利用蚁群算法的搜索规则，将链路负载均衡度、流接受率、时延和丢包率作为蚂蚁选择下一节点的影响因素，在多个约束条件下，获得传输的最佳路径。理论分析及仿真结果说明，所提出的算法具有较好的负载平衡能力，而且可以提高网络的服务质量。     

蚁群优化算法在物流车辆调度系统中的应用

根据对蚁群算法进行的深入研究,指出了蚁群算法在解决大型非线性系统优化问题时的优越性。通过仔细分析遗传算法和粒子群算法在解决物流车辆调度系统问题的不足之处,基于蚁群算法的优点,并根据物流车辆调度系统自身的特点,对基本蚁群算法进行适当的改进,给出算法框架。并且以线性规划理论为基础,建立物流车辆系统的数学模型,给出调度目标与约束条件,用改进后的蚁群算法求解物流车辆调度系统的问题,求得最优解,根据最优解和调度准则进行实时调度。使用Java语言编写模拟程序对比基于改进粒子群算法和改进蚁群算法的调度程序。通过对比证明了所提出的改进蚁群算法解决物流车辆调度优化问题的正确性和有效性