Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.

Complex task planning method of space-aeronautics cooperative observation based on multi-layer interaction

With the new development trend of multi-resource coordinated Earth observation and the new goal of Earth observation application of“short response time,high observation accuracy,and wide coverage”,space-aeronautics cooperative complex task planning problem has become an urgent problem to be solved.The focus of this problem is to use multiple resources to perform collaborative observations on complex tasks.By analyzing the process from task assignment to receiving task observation results,we propose a multi-layer interactive task planning framework which is composed of a preprocessing method for complex tasks,a task allocation layer,a task planning layer,and a task coordination layer.According to the characteristics of the framework,a hybrid genetic parallel tabu(HGPT)algorithm is proposed on this basis.The algorithm uses genetic annealing algorithm(GAA),parallel tabu(PT)algorithm,and heuristic rules to achieve task allocation,task planning,and task coordination.At the same time,coding improvements,operator design,annealing operations,and parallel calculations are added to the algorithm.In order to verify the effectiveness of the algorithm,simulation experiments under complex task scenarios of different scales are carried out.Experimental results show that this method can effectively solve the problems of observing complex tasks.Meanwhile,the optimization effect and convergence speed of the HGPT is better than that of the related algorithms.

Cooperative driving model for non-signalized intersections with cooperative games

Cooperative driving around intersections has aroused increasing interest in the last five years.Meanwhile,driving safety in non-signalized intersections has become an issue that has attracted attention globally.In view of the potential collision risk when more than three vehicles approach a non-signalized intersection from different directions,we propose a driving model using cooperative game theory.First,the characteristic functions of this model are primarily established on each vehicle’s profit function and include safety,rapidity and comfort indicators.Second,the Shapley theorem is adopted,and its group rationality,individual rationality,and uniqueness are proved to be suitable for the characteristic functions of the model.Following this,different drivers’characteristics are considered.In order to simplify the calculation process,a zero-mean normalization method is introduced.In addition,a genetic algorithm method is adopted to search an optimal strategy set in the constrained multi-objective optimization problem.Finally,the model is confirmed as valid after simulation with a series of initial conditions.

Competitive location problem of multi-level pickup point considering cooperative coverage

To occupy a greater market share in terminal distribution, companies are urged to make full use of cooperative coverage formed with brand effect and information sharing in the layout of pickup points. Based on the diversity of pickup points, the piecewise function, signal intensity function and probability function are introduced. Meanwhile, considering the effect of distance satisfaction and cooperation coverage on customer behavior, the location model of the pickup point under competitive environments is established. The genetic algorithm is used to solve the problem, and the effectiveness of the model and algorithm is verified by a case. The results show that the sensitivity of weighted demand coverages to budget decreases gradually. The maximum weighted demand coverage increases at first and then decreases with the increase of the signal threshold, and there is a positive correlation with the change of the actual demand coverage to the senior customers, but it is negatively related to the intermediate and primary customers. When the number of high-level pickup points in a competitive enterprise is small, the advantage of the target enterprise is more significant. Through comparison, the cooperative coverage model is better than the non-cooperative coverage model, in terms of the weighted demand coverage, the construction cost and the attention paid to the important customers.

Cooperative Perception Optimization Based on Self-Checking Machine Learning

In the process of spectrum perception,in order to realize accurate perception of the channel state,the method of multi-node cooperative perception can usually be used.However,the first problem to be considered is how to complete information fusion and obtain more accurate and reliable judgment results based on multi-node perception results.The ideas put forward in this paper are as follows:firstly,the perceived results of each node are obtained on the premise of limiting detection probability and false alarm probability.Then,on the one hand,the weighted fusion criterion of decision-making weight optimization of each node is realized based on a genetic algorithm,and the useless nodes also can be screened out to reduce energy loss;on the other hand,through the linear fitting ability of RBF neural network,the self-inspection of the perceptive nodes can be realized to ensure the normal operation of the perceptive work of each node.What's more,the real-time training data can be obtained by spectral segmentation technology to ensure the real-time accuracy of the optimization results.Finally,the simulation results show that this method can effectively improve the accuracy and stability of channel perception results,optimize the structure of the cooperative network and reduce energy consumption.

A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.

酿酒葡萄生长全过程多任务多农机调度研究

针对酿酒葡萄生长全过程中存在多任务多农机调度的需求,而传统调度研究很少有针对酿酒葡萄的多农机多任务协同作业研究。为此,结合作业田块相关信息与农机信息,基于车辆路径规划问题(Vehicle Routing Problem,VRP),以成本最优为目标构建酿酒葡萄生长全过程多任务多农机调度模型,提出改进自适应遗传算法(Improved Adaptive Genetic Algorithm,IAGA)进行运算求解;结合宁夏贺兰山东麓酿酒葡萄产区实际作业田块信息与农机信息进行仿真实验,并与传统遗传算法(Genetic Algorithm,GA)进行对比。研究结果表明:相比于GA,IAGA有着较强的收敛性,不易陷入局部最优,在调度结果上,调度总时间上能够缩短2.22%,在农机调度总成本降低2.32%,在酿酒葡萄实际作业中能够极大地节约作业时间并降低作业成本。

Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modifed genetic algorithm with multi-type genes

The task assignment problem of multiple heterogeneous unmanned aerial vehicles （UAVs）, concerned with cooperative decision making and control, is studied in this paper. The heterogeneous vehicles have different operational capabilities and kinematic constraints, and carry limited resources （e.g., weapons） onboard. They are designated to perform multiple consecutive tasks cooperatively on multiple ground targets. The problem becomes much more complicated because of these terms of heterogeneity. In order to tackle the challenge, we modify the former genetic algorithm with multi-type genes to stochastically search a best solution. Genes of chromo- somes are different, and they are assorted into several types according to the tasks that must be performed on targets. Different types of genes are processed specifically in the improved genetic operators including initialization, crossover, and mutation. We also present a mirror representation of vehicles to deal with the limited resource constraint. Feasible chromosomes that vehicles could perform tasks using their limited resources under the assignment are created and evolved by genetic operators. The effect of the proposed algorithm is demonstrated in numerical simulations. The results show that it effectively provides good feasible solutions and finds an optimal one.

HEURISTIC QUANTUM GENETIC ALGORITHM FOR AIR COMBAT DECISION MAKING ON COOPERATIVE MULTIPLE TARGET ATTACK

In order to achieve the optimal attack outcome in the air combat under the beyond visual range(BVR)condition,the decision-making(DM)problem which is to set a proper assignment for the friendly fighters on the hostile fighters is the most crucial task for cooperative multiple target attack(CMTA).In this paper,a heuristic quantum genetic algorithm(HQGA)is proposed to solve the DM problem.The originality of our work can be supported in the following aspects:(1)the HQGA assigns all hostile fighters to every missile rather than fighters so that the HQGA can encode chromosomes with quantum bits(Q-bits);(2)the relative successful sequence probability(RSSP)is defined,based on which the priority attack vector is constructed;(3)the HQGA can heuristically modify quantum chromosomes according to modification technique proposed in this paper;(4)last but not the least,in some special conditions,the HQGA gets rid of the constraint described by other algorithms that to obtain a better result.In the end of this paper,two examples are illustrated to show that the HQGA has its own advantage over other algorithms when dealing with the DM problem in the context of CMTA.

基于改进离散模拟退火遗传算法的雷达网协同干扰资源分配模型

针对分布式干扰机掩护目标突防雷达网背景下的干扰资源分配问题,提出了一种引入随机密钥的改进离散模拟退火遗传算法(improved discrete simulated annealing genetic algorithm,IDSA-GA)对资源分配过程进行优化。基于雷达网融合检测概率构建干扰效果评估函数,利用IDSA-GA对函数寻优求解。IDSA-GA在模拟退火遗传算法(simulated annealing genetic algorithm,SA-GA)的基础上引入随机密钥,完成算法的离散化;并在迭代的过程中增加记忆功能,克服了过早收敛的现象。仿真结果表明,与GA相比,提出的IDSA-GA收敛迅速,寻优能力强,能有效解决干扰资源优化分配问题。

汽车门板内饰多机器人焊接的动态协同规划

为了减小多机器人协同焊接的路径长度并提高机器人之间的负载均衡度,提出了基于动态规划-个体差异进化遗传算法的协同焊接规划方法。以多机器人协同焊接路径长度、负载均衡度为优化目标建立了优化模型,并分析了协同焊接约束条件。针对单机器人焊接路径规划问题,在遗传算法中针对染色体进化能力的差异性,提出了个体差异进化策略,给出了基于个体差异进化遗传算法的路径规划方法。针对多机器人协同焊接问题,使用动态规划将其划分为3个子问题,实现了多机器人协同焊接任务分配和路径规划。经某型汽车前门焊点路径规划验证,个体差异进化遗传算法规划的路径最佳长度、平均长度均优于传统遗传算法;经后门焊点的4机器人协同焊接验证,在满足无干涉约束下,这里方法的路径长度、负载均衡度优于文献[11]离散粒子群算法。实验验证了这里方法在多机器人协同焊接分配和规划问题中的优越性。

苹果园内无人割草机多机协同作业路径优化算法

[目的]提高新型苹果园内多台无人割草机协同作业时的工作效率。[方法]提出一种改进的遗传算法(Improved genetic algorithm, IGA),为每台割草机分配并优化作业路径。根据实际无人割草机作业情况,以总转弯时间和作业时长为综合优化目标,构建无人割草机多机作业路径优化模型。通过设定任务阈值,引入改良圈策略和Metropolis准则改进遗传算法(Genetic algorithm, GA)以求解模型。[结果]仿真试验结果表明,IGA为每台割草机分配的任务量均衡,与GA相比,IGA优化后的矩形果园路径平均总转弯时间和作业时长分别减少22.89%和19.36%;与分区作业相比,IGA优化后的矩形果园路径平均总转弯时间和作业时长分别减少45.53%和10.68%。在梯形果园中,IGA不受果树分布影响,与GA和分区作业相比,平均总转弯时间分别减少14.38%和34.08%,平均作业时长分别减少23.71%和10.07%。[结论]所提出的IGA性能更好,能有效优化机群的作业路径,缩短作业时长,提高作业能力。

多机协同对抗雷达组网的干扰资源分配研究

在现代信息化战场上,相对于传统的雷达各自独立探测和制导的作战方式,多雷达组网探测预警的发展趋势日益明显,雷达组网后对突防飞机的安全造成了巨大威胁。远距支援干扰飞机在掩护突防编队作战过程中,为了发挥有限干扰资源的最大效能以保证突防飞机的安全,通过对多机协同干扰对抗雷达组网作战模式的分析,以突防飞机生存概率作为目标函数,建立约束条件下的干扰资源分配模型。重点分析了突防全过程的干扰掩护情况,利用遗传算法解决了最优资源分配方案求解的问题。仿真结果表明,突防飞机在支援干扰飞机协同掩护下的生存概率为96.72%。分配结果满足突防作战的要求,合理的分配干扰资源对充分发挥远距支援干扰飞机的协同作战能力作用显著。

基于遗传算法的除草机器人多机路径规划研究

【目的】针对单个小型行间除草机器人续航能力差,无法独立完成大面积稻田除草任务的问题,提出了一种基于多染色体优化遗传算法(multi-chromosome optimized genetic algorithm,MGA)的多机协同路径规划方法。【方法】首先,根据稻田秧苗分布情况,将能耗最高的除草机器人的移动距离最小化作为优化目标,建立了多机协同路径规划模型;其次,设计了一种多染色体遗传算法,并引入逐代竞争机制、配对交换机制及自适应变异算子,以提升算法的最优解质量和鲁棒性;最后,在不同田形的模拟地图和稻田栅格地图上进行了多机协同路径规划仿真试验,并与传统遗传算法(genetic algorithm,GA)及自适应遗传算法(adaptive genetic algorithm,AGA)进行对比。【结果】在不同田形地图仿真试验中,多染色体优化遗传算法表现出了较高的最优解质量和鲁棒性,明显优于传统遗传算法和自适应遗传算法。在稻田栅格地图仿真试验中,优化遗传算法生成的能耗最高的除草机器人的移动距离相较于传统遗传算法缩短了9.7%,相较于自适应遗传算法缩短了8.0%;平均路径长度相较于传统遗传算法缩短了6.1%,相较于自适应遗传算法缩短了3.8%。搜索结果的标准差相较于传统遗传算法降低了34%,相较于自适应遗传算法降低了40%。【结论】多染色体优化遗传算法能有效缩短能耗最高的小型行间除草机器人的移动距离,有助于其在续航能力范围内完成作业任务。

多弹集群协同优化决策算法研究

本文以集群对海作战为应用背景,为重点解决多弹多空间散布的时空构型及目标分配决策问题,建立了多弹典型协同场景下的导弹主动感知协同探测模型,设计了导弹协同探测与协同攻击的模型以及相应的算法。首先,建立了导弹三自由度和舰船二自由度模型、导弹飞行能力评估模型、导弹突防概率评估模型和导弹威胁度评估模型,使得对导弹执行探测和打击任务中能力的变化度量更加精确。其次,设计导弹在不完全信息条件下的主动感知协同探测模型和协同攻击模型,保证导弹协同作战的效益最大化,最大限度地打击敌方势力。再次,针对导弹协同打击目标分配和导弹编队构型问题,使用融合了遗传算法的粒子群(GAPSO)算法问题进行求解,并与传统的粒子群(PSO)算法进行了对比,实现了多方位考虑战场态势信息,对敌方目标进行探测攻击一体化打击。最后,对建立的作战模型进行仿真,得到算法仿真图和算法多次仿真的统计数据。相比其他算法,该算法具有一定的优越性,大幅提升了导弹的智能化对抗水平和突防打击能力。

A Cooperation-planning Model Based on Bilevel Programming Decision

This paper is based on a resource constrained active network project;the constraint of the local resource and the time constraint of the cooperation resource are considered simultaneously.And the respective benefit of the manager and cooperation partners is also considered simultaneously.And a cooperation planning model based on bilevel multi-objective programming is de- signed,according to the due time and total cost.And an extended CNP based on the permitted range for resource and time requests is presented.A larger task set in scheduling cycle is on the permitting for the request of cooperation resource and time while the task manager itself may be permitted biding for tasks.As a result,the optimization space for the cooperation planning is enlarged.So not every bidding task is successfully bid by invitee,and the task manager itself takes on some bidding tasks.Finally,the genetic algorithm is given and the validity and feasibility of the model is proved by a case.

考虑同时取送货的车机协同路径优化问题

考虑到传统同时取送货问题模式单一,无法应对复杂多变情况的现实需要,研究了一种考虑同时取送货的路径优化问题(vehicle routing problem with drones for simultaneous pickup and delivery, VRPD-SPD)。首先,以车辆与无人机总成本最小为优化目标,建立了考虑无人机单架次访问顺序约束的混合整数线性规划模型。其次,提出了一种基于遗传思想的两阶段启发式算法(two-stage heuristic algorithm based genetic, TSHAG),第一阶段结合贪婪算法和节约算法生成初始解,第二阶段通过改进的遗传算法优化初始解,设计了多元组编码方式来提高解码效率,改进了交叉算子来增加邻域解的搜索空间,设计了新的变异算子来提高算法全局寻优性能。最后,算例实验结果表明了TSHAG算法能够有效地解决VRPD-SPD问题。

基于博弈建模的地对空防御火力分配策略选择

战场环境复杂多变,如何根据当前态势及火力资源特点,及时有效地对来袭目标进行火力分配,是防空指挥中的关键环节。针对地对空防御问题,考虑对敌方来袭目标的毁伤程度和我方武器资源消耗因素,以最大化总体毁伤概率和最小化使用武器价值为目标建立多目标优化模型。由于优化目标之间存在对武器资源的竞争,以优化目标为博弈方,以决策武器如何攻打来袭目标为策略,建立非合作博弈模型,并结合禁忌搜索技术,设计基于纳什均衡搜索的改进遗传算法(NE-IGA)进行求解。实验结果表明,与求解优化模型的基于禁忌搜索的改进遗传算法(TSGA)及基本遗传算法(GA)相比,博弈模型及其求解算法NE-IGA能够得到更优的分配方案。

基于遗传-蚁群融合算法的干扰资源分配方法

针对多部干扰机协同干扰多部雷达的干扰资源分配问题,提出一种基于遗传-蚁群融合算法的干扰资源分配算法。首先采用综合集成赋权法结合逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)对目标雷达进行威胁评估,然后建立干扰资源多约束优化分配模型,最后采用遗传-蚁群融合算法对模型进行求解。融合算法利用遗传算法快速寻找出若干组优化解,将这些优化解用于调整蚁群算法中初始信息素的分布,利用蚁群算法对问题进一步优化,从而找到最优解,提升了算法的求解精度和求解时间。仿真结果表明,融合算法的性能在收敛速度和寻优准确性等方面相较于其他算法都有了较大提升。

基于并行协同的多车间协同调度问题研究

传统企业在实际生产中,其多个关联车间之间的生产计划与调度存在难以协作的问题。为此,针对多车间协同调度问题建立了调度模型,提出了一种多车间协同调度的并行协同进化遗传算法(PCE-GA),并且采用该算法对上述模型进行了求解。首先,以最小化订单完工时间为目标,建立了单目标调度模型;然后,采用了并行协同进化遗传算法,对上述单目标调度模型进行了求解,基于工件、机器、装配关系的三层整数编码的染色体编码方案,提出了一种协同适应度值计算的方法;最后,以某液压缸生产企业为例,针对单目标调度问题,采用该算法与单车间遗传算法(JSP-GA)、并行协同模拟退火算法(PCE-SA)分别进行了求解,并对其结果进行了比较,以验证PCE-GA算法的优越性。研究结果表明:采用PCE-GA算法得到的优化率为13.3%,比单车间作业调度遗传算法求解的数据优化11.5%,该结果证明了PCE-GA算法在解决多车间协同优化问题时的优越性。