A review of unmanned vehicle distribution optimization models and algorithms

With the development of globalization and artificial intelligence,as well as the outbreak of COVID-19,unmanned vehicles have played an important role in cargo distribution.In order to better analyze the research directions of unmanned vehicle distribution,this paper summarizes the models and algorithms of unmanned vehicle distribution optimization.The research results show that most of the studies have established the goal of optimizing the total costs or travel time.Many researchers have begun to study multi-objective optimization problems,but there are certain limitations,so some studies convert these problems into single-objective optimization for solving,such as converting time and energy consumption into cost,waiting time into distance,and time delay into penalty cost.With the development of unmanned vehicle distribution technology,in future research,a multi-objective model with the lowest cost,the shortest distance and the best security should be established and solved.Most studies have proposed heuristic algorithms for solving the unmanned vehicle distribution problem,and improved optimization solutions have been obtained.In order to ensure the diversity of solution methods,and give consideration to solution time and solution quality,hybrid methods with other algorithms will be a future research direction,for example,the combination of heuristic algorithm and exact algorithm.With the gradual deepening of research,integrated distribution of multiple types of unmanned equipment will become the focus of future research.

Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm

A distribution network plays an extremely important role in the safe and efficient operation of a power grid.As the core part of a power grid’s operation,a distribution network will have a significant impact on the safety and reliability of residential electricity consumption.it is necessary to actively plan and modify the distribution network’s structure in the power grid,improve the quality of the distribution network,and optimize the planning of the distribution network,so that the network can be fully utilized to meet the needs of electricity consumption.In this paper,a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm.For the distribution network structure planning of dual power sources,the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies,and the dual power distribution network structure model is established based on the principle of the lowest cost.The artificial ants in the algorithm were compared with real ants in nature,and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem(TSP).Then,the limitations of the ant colony algorithm were analyzed,and an improvement strategy was proposed by using python for digital simulation.The results demonstrated the reliability of model-building and algorithm improvement.

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 algorithm for the traveling salesman problems

Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.

Novel Voltage Scaling Algorithm Through Ant Colony Optimization for Embedded Distributed Systems

Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results. However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. A new algorithm is presented, which is based on ant colony optimization, called ant colony optimization voltage and task scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algorithm. Both of them generate quite good results, saving up to 30% more energy than that of the previous ones under coarse-grained modes, and their results don’t depend on the number of modes available.

Research on Ant Colony Algorithm in Vehicle Operation Adjustment Based on IOT

Automatic solution of vehicle operation adjustment is the important content in realizing vehicle traffic command automation on Internet of Things platform. Based on both the organization realization of Internet of Things platform and the merging vehicle operation adjustment into the Flow-Shop scheduling problem in manufacturing systems,this paper has constructed the optimization model with a two-lane vehicle operation adjustment. With respect to the large model solution space and complex constraints,a better solution algorithm is proposed based on ant colony algorithm for optimal quick solution. The simulation results show that the algorithm is feasible and the approximate optimal solution can be quickly obtained.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Buffer and Wiresizing Optimization under the Distributed RLC Model with Crosstalk Constraint

In this paper, we study the interconnect buffer and wiresizing optimization problem under a distributed RLC model to optimize not just area and delay, but also crosstalk for RLC circuit with non-monotone signal response. We present a new multiobjective genetic algorithm（MOGA） which uses a single objective sorting（SOS） method for constructing the non-dominated set to solve this multi-objective interconnect optimization problem. The MOGA/SOS optimal algorithm provides a smooth trade-off among signal delay, wave form, and routing area. Furthermore, we use a new method to calculate the lower bound of crosstalk. Extensive experimental results show that our algorithm is scalable with problem size. Furthermore, compared to the solution based on an Elmore delay model, our solution reduces the total routing area by up to 30%, the delay to the critical sinks by up to 25%, while further improving crosstalk up to 25.73% on average.

机器人人工拣选环境下混流装配线齐套物料配送优化

为了提高汽车制造企业配送的智能化水平、解决混流装配线齐套物料人工拣选效率低、成本高的问题,引入基于齐套物料配送策略的机器人人工拣选模式,通过优化自动拣选机器人、工人的配置数量及配送周期,使包含机器人使用成本、劳动力成本和在制品库存成本在内的总成本最小。为求解该配送优化问题,提出了改进量子蚁群算法,利用量子比特的叠加性增加种群多样性,避免算法陷入局部最优,同时设计了改进量子旋转门更新机制和基于差分进化操作的非最优个体优化策略,提高了算法收敛速度和寻优质量。最后,通过算例分析验证了模型的正确性与算法的有效性,并分析了拣选批量对总成本的影响。

基于BIM的PC构件排样优化设计

针对PC构件排样与上游装配式建筑设计模型的协同效用问题,进行了基于BIM的PC构件排样优化设计研究。分析了装配式BIM中PC构件的组成元素,完成PC构件信息的提取;结合改进蚁群算法与动态剩余矩形算法对PC构件排样进行优化,采用遗传变异策略与路径选择机制改进算法后期易陷入局部最优的不足,经过多次优化迭代得出最优排样。某PC构件企业的生产排样实例验证了基于BIM的PC构件排样优化方案可实现上游装配式建筑设计模型与PC构件排样的协同效用,提高了排样效率。

基于改进蚁群算法的机器人全局路径规划

针对传统蚁群算法存在初始信息素缺乏、收敛速度慢以及无法有效躲避障碍物等问题,文中提出了一种基于改进蚁群算法的全局路径规划。引入正态分布函数改进传统启发函数,提高了算法效率,缩短了算法收敛所需时间。自适应调整信息素挥发系数,限定信息素范围,避免过早收敛。对算法路径平滑处理,缩短路径长度,从而实现机器人的全局路径规划。仿真结果表明,在20×20环境下,文中算法平均迭代次数比传统蚁群算法减少了28代,收敛速度更快。平均拐点减少了33.3%,使路径更为平滑,克服了初始信息素缺乏,加快了收敛速度,减少了拐点数量,能够有效躲避环境中的障碍物,证明了该算法的可行性。

基于改进蚁群算法的邮船舱室模块移运路径规划

针对大型邮船舱室模块运输过程中存在的移运路线长、路线混乱、舱室模块易与障碍物发生碰撞等问题,提出应用加入动态搜索模型的蚁群算法对邮船舱室模块进行路线规划,为运输舱室模块提供清晰、便捷的移运路线。对主竖区的障碍物进行分析,建立模拟实际工况的栅格地图,采用改进蚁群算法寻找移运路径。对不同位置所经过的栅格地图和蚁群数量进行动态调整。采用模拟退火算法寻找蚁群算法的参数。采用离散点分析确定移运路径的主、支通道。仿真试验结果表明,应用改进蚁群算法建立主、支通道进行舱室模块移运可有效提高舱室模块的运输效率。

基于改进蚁群算法的低碳冷链配送路径优化

针对目前冷链配送路径优化中存在的综合配送成本考虑不全面的问题,根据生鲜配送特点,综合考虑基本运输成本、碳排放、制冷、货损以及时间窗约束,建立以综合配送成本最小化为目标的路径优化模型。提出改进蚁群算法对该优化模型进行求解,在初始阶段使用遗传算法生成初期信息素分布,而后使用蚁群算法进行后续寻优搜索,再引入模拟退火算法的Metropolis准则筛选优质解。通过仿真实验验证了优化模型和改进算法的有效性,对低碳可持续发展理念下生鲜品冷链配送路径优化问题研究具有一定意义,助力冷链运输行业向低碳经济转型。

一种多策略改进鲸鱼优化算法的混沌系统参数辨识

针对混沌系统参数辨识精度不高的问题,以鲸鱼优化算法(whale optimization algorithm,WOA)为基础,提出一种多策略改进鲸鱼优化算法(multi-strategy improved whale optimization algorithm,MIWOA)。采用Chebyshev混沌映射选取高质量初始种群,采用非线性收敛因子和自适应权重,提高算法收敛速度,为了避免算法陷入局部最优,动态选择自适应t分布或蚁狮优化算法更新后期位置,提高处理局部极值的能力。通过对10个基准函数和高维测试函数进行仿真试验,表明MIWOA具有良好的稳定性和收敛精度。将MIWOA应用于辨识Rossler和Lu混沌系统参数,仿真结果优于现有成果,表明本文MIWOA辨识混沌系统参数的高效性和实用性。

改进分布估计算法的AUV全局路径规划

为解决基于固定宽度直方图(FWH)的分布估计算法在解决自主水下机器人(AUV)路径规划问题时易陷入局部最优的问题,提出一种个体更新方法融合策略。将狼群优化算法(WPA)中的部分个体更新方法,以及三次样条函数融合到算法当中,增加算法个体更新方法的多样性。提高AUV路径规划的精度,增加收敛速度。在水下数字高程模型环境中,对原始算法以及改进后的算法进行性能评估。仿真结果验证了算法改进的可行性和有效性。

基于改进粒子群算法的分散式电采暖负荷模型参数辨识

分散式电采暖负荷是一种具有巨大调节潜力的需求响应资源,但是,用户行为差异、太阳辐照度等随机因素为建立精确的分散式电采暖负荷模型增加了一定难度。首先,结合具体应用场景,搭建基于气候模拟平台的分散式电采暖负荷实验平台,采集负荷建模实验数据。然后,针对分散式电采暖负荷二阶等效热参数模型方程较为复杂的问题,以仿真温度与实测温度之间的误差最小为目标,提出基于改进粒子群算法的模型参数辨识方法,建立分散式电采暖负荷模型。最后,通过改变数据采样周期,对负荷模型进行优化。结果表明,所提参数辨识及建模优化方法可以有效地提高分散式电采暖负荷二阶等效热参数模型精度。

海洋应急救援站点选址和资源配置优化

应急救援站点选址与资源配置问题是海上应急站点管理领域中的一个重要研究方向。已有方法针对的是确定资源配置的选址问题或确定选址的资源配置问题,而未同时考虑这两者的结合问题。鉴于此,针对应急救援站选址与资源配置的综合问题进行了研究。首先采用基于距离衰减函数的两步移动搜索法,以满足事故点资源需求和提高事故点资源获取效率为目标建立了多目标优化模型;然后利用蚁群算法和分布估计算法的优点,设计求解上述模型的蚁群分布估计算法,生成站点选址与资源配置方案;最后,在仿真实验上,验证了模型的可行性和算法的有效性。所提模型与算法可以解决海洋应急救援站点及其他类似领域的选址与资源配置优化问题。

基于智能优化算法的PCB板电子元件条件热布局优化及仿真分析

针对PCB板电子元件的热布局优化问题提出一种基于智能优化算法的方法,对PCB板电子元件的热特性进行分析及建模,对其条件热布局的目标函数和约束条件进行限制,将智能优化算法引入热布局优化过程中,求得最优的元件布局方案,采用蚁群算法,通过对比实验验证方法的有效性,此结果对提高PCB板电子元件的热性能具有重要的理论和实际意义,可为同类问题的研究提供参考。

基于量子蚁群算法的配电网故障区段快速定位技术

分布式电源并入配电网已成为新型电力系统重要特征之一,分布式电源的接入与发电的不确定性使配电网潮流复杂多变,对配电网故障快速定位提出更高的技术要求.然而,现有智能优化算法在解决配电网故障区段定位问题时会出现收敛速度慢、易陷入局部最优等问题.针对这些挑战与问题,提出一种基于量子蚁群算法(QACA)的配电网故障区段快速定位技术.首先,根据状态逼近思想和最小故障集理论构建配电网故障定位的数学模型;其次,针对馈线终端单元上传信息缺失情况提出信息自修正方法,并提出分级定位模型来缩短定位时间;然后,提出3种改进技术对QACA进行针对性改进,改进量子旋转门更新机制,以函数控制形式动态调整旋转角大小,同时引入精英策略加快算法收敛速度.最后,在关键参数确定后验证了改进技术、信息自修正法、分级定位模型的有效性.将所提算法与7种不同算法进行对比,结果表明:改进的QACA可有效完成故障区段定位,具有良好的收敛速度、准确率以及容错性能.