An Effective Optimization Algorithm for Ant Colony Vehicular Congestion Management

Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.

Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System

The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.

Ant colony algorithm based on genetic method for continuous optimization problem

A new algorithm is presented by using the ant colony algorithm based on genetic method （ACG） to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of component, trail information and fitness. The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed. The genetic method is used to form new solutions from the solutions got by the ants. Best solutions are selected to update the seeds in the sets and trail information of the seeds. In updating the trail information, a diffusion function is used to achieve the diffuseness of trail information. The new algorithm is tested with 8 different benchmark functions.

Ant Colony Optimization Based on Adaptive Volatility Rate of Pheromone Trail

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper first proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. Second, the strategy is combined with the setting of other parameters to form a new ACO method. Then, the proposed algorithm can be proved to converge to the global optimal solution. Finally, the experimental results of computing traveling salesman problems and film-copy deliverer problems also indicate that the proposed ACO approach is more effective than other ant methods and non-ant methods.

Electro-Hydraulic Servo System Identification of Continuous Rotary Motor Based on the Integration Algorithm of Genetic Algorithm and Ant Colony Optimization

In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO.

Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.

Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)

The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design

An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.

Algorithm for Low Altitude Penetration Aircraft Path Planning with Improved Ant Colony Algorithm

The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained.

Adaptive optimization of agile organization of command and control resource

Adaptive optimization is one of the means that agile organization of command and control resource （AOC2R） adapts for the dynamic battlefield environment. A math model of the adaptive optimization of AOC2R is put forward by analyzing the interrelating concept and research. The model takes the adaptive process as a multi-stage decision making problem. The 2-phases method is presented to calculate the model, which obtains the related parameters by running the colored Petri net （CPN） model of AOC2R and then searches for the result by ant colony optimization （ACO） algorithm integrated with genetic optimization techniques. The simulation results demonstrate that the proposed algorithm greatly improves the performance of AOC2R.

基于改进遗传算法对机械臂最优时间轨迹规划

针对传统工业机器人在轨迹规划过程中,运动耗时长、易陷入局部最优解的问题,提出一种基于改进自适应遗传算法对于6R机械臂轨迹优化算法。通过加入改进的自适应调节机制,自适应的去改变交叉概率和变异概率。首先,建立六自由度机械臂模型,采用改进型D-H参数法获得机器人连杆参数数据;其次,通过4-1-4多项式插值的方法进行轨迹规划,以运行时间为优化目标,利用改进自适应遗传算法结合蚁群算法对运动轨迹进行优化;最后,通过目标函数解决运动学约束问题。通过MATLAB仿真实验验证相比于传统的遗传算法,该轨迹的运行时间从12.23 s减少到了9.05 s,整体运行轨迹时间缩短3.18 s,优化后的效率提高近26%。适应度提高1.73,证明该算法能够有效地加快轨迹的运行时间,提高了机械臂的工作效率。

皮革数控裁剪路径优化算法研究综述

裁剪路径优化是皮革数控裁剪加工的关键,是确保加工高效率与皮革高利用率的主要途径。为提高皮革数控裁剪加工效率,缩短裁剪空行程路径,减少皮革边角料浪费,各研究领域分别着手于裁剪路径全局性与算法高效性,提出皮革数控裁剪路径优化算法。本文通过对比分析皮革数控裁剪与人工裁剪,综述了皮革数控裁剪路径优化遗传算法、蚁群算法、模拟退火算法,以及改进算法、混合优化算法等相关研究现状与成果,并在此基础上对皮革数控裁剪路径优化算法研究的不足进行了总结。

考虑电动汽车充电负荷及储能寿命的充电站储能容量配置优化

提出了一种优化电动汽车充电站储能容量配置的方法。该方法考虑了季节性电动汽车充电负荷波动与光伏出力之间的关系,并且考虑了储能寿命。论文利用蒙特卡罗法考虑了不同类型电动汽车的多种影响因素,对整体负荷进行预测。以每日运行成本最低为优化目标,在考虑四季光伏出力和储能寿命的影响下,采用了3种算法对目标函数进行优化,以得到最佳的光储充电站储能配置方案。研究以西北某地区为例。结果表明:冬季下综合成本为3.0432×10^(6)元,相比于其余3个季节综合成本最低;采用遗传算法时,在综合成本相差不多时,获得的储能配置最优,储能容量为22.82 MWh,储能功率为7.31MW,从而得到光储充电站最优的储能容量配置。

基于改进蚁群算法的多无人机协同任务分配

针对城市物流场景下多无人机协同任务分配问题,考虑无人机性能、飞行成本和配送点紧迫度不同,建立更加符合真实场景的组合优化模型,提出了一种融合遗传算法的改进蚁群算法。基于无人机和配送点之间的访问关系,根据遗传算法中基因编码思想采用了一种整数组合基因编码方式以生成种群个体,为提高算法搜索能力设计了一种扰动算子的改进交叉操作。将遗传算法的结果转化为蚁群算法的初始信息素,通过一种自适应信息素机制和引入扩展启发量的策略来指导种群搜索方向,从而平衡算法的全局搜索能力和局部搜索能力。仿真实验表明,所提出的改进算法能很好的跳出局部最优,并且能够高效、稳定地找出合理的无人机配送方案。

基于遗传-蚁群优化算法的QoS组播路由算法设计

为了提高网络路由性能,提出并设计了一种基于遗传-蚁群优化算法的服务质量(quality of service,QoS)组播路由算法。首先,设计了自适应变频采集策略用于采集网络与节点信息,以此获得网络和节点的状态,为后续路由优化提供数据支持;其次,计算路径代价,将路径代价最小作为优化目标,建立QoS组播路由优化模型,并设置相关约束条件;最后,结合遗传算法和蚁群算法提出一种遗传-蚁群优化算法求解上述模型,输出最优路径,完成路由优化。实验结果表明,所提算法可有效降低路径长度与路径代价,提高搜索效率与路由请求成功率,优化后的路由时延抖动较小。

基于线性加权和法的装配线平衡问题求解

针对生产节拍确定条件下以提高装配线平衡程度为目的的装配线平衡问题,将装配线平滑系数和装配线平衡率作为优化目标,考虑装配作业分配、工作站数量等因素,使用线性加权和法,以两个优化目标的优先占比作为权重参数建立单目标装配线平衡优化模型;对遗传算法(Genetic Algorithm,GA)和蚁群(Ant Colony Optimization,ACO)算法的混合算法进行改进,构造新的适应度函数和距离信息矩阵对模型进行求解;最后对经典算例进行数值实验,实验结果与以往算法结果比较,平衡程度改进均值提高了5%,表明改进的模型及算法可以更好地提高装配线的平衡程度,验证了模型及算法的有效性。

无线传感器网络中基于能量优化的路由协议ANT-LEACH

经典路由协议LEACH采用自适应分簇算法,簇头与基站直接通信,因此一旦二者距离较远,则这种单跳传输方式将消耗较多能量,并最终导致整个网络运行失效。提出一种改进的基于能量优化的路由协议ANT-LEACH,该协议将蚁群优化算法融入到簇头选路过程中,重点引入引力度函数概念对蚁群选择概率公式和信息素更新规则进行改进,充分考虑簇头节点的剩余能量,在簇头与基站之间找到一条能量最优路径,变单跳为多跳传输方式。仿真结果表明该协议有效地降低了节点能耗,延长了网络的生存时间,并保证了整个网络负载的平衡。

融合AntNet与遗传算法的动态网络路由算法

提出了一种新的动态分布式网络路由算法。在AntNet算法中引入了路径遗传运算(GA),提出了新的信息素更新策略。对蚂蚁发现的路径进行染色体编码,并用适应度函数对其进行适应度评价,通过路径交叉和路径变异运算以及种群的不断进化,来提高解的质量。仿真结果表明,所提出的算法能快速收敛,且有效地提高了网络吞吐量、降低了平均延时。