基于组合遗传算法的电力科技创新成果多agent集成仿真

电力科技成果普遍交叉,为了降低集成电力科技创新成果耗时与能量损耗,研究基于组合遗传算法的电力科技创新成果多agent集成仿真。将底层数据库中电力科技创新资源经多种接口调配至关联库、综合评价以及专家评价三大agent模块,通过结合遗传算法与蚁群算法优点的组合遗传算法,优化解决多agent集成任务分配问题,实现电力科技创新成果高效集成。实验表明,该算法的寻优能力高、求解速度快;可规范化、结构化地集成电力科技创新成果信息;具有耗时少、能量损耗低优势。为科技创新和成果转化提供新思路。

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

Step-coordination Algorithm of Traffic Control Based on Multi-agent System

Aiming at the deficiency of conventional traffic control method, this paper proposes a new method based on multi-agent technology for traffic control. Different from many existing methods, this paper distinguishes traffic control on the basis of the agent technology from conventional traffic control method. The composition and structure of a multi-agent system （MAS） is first discussed. Then, the step-coordination strategies of intersection-agent, segment-agent, and area-agent are put forward. The advantages of the algorithm are demonstrated by a simulation study.

A New Algorithm for Resource Constraint Project Scheduling Problem Based on Multi-Agent Systems

The resource constrained project scheduling problem (RCPSP) and a decision-making model based on multi-agent systems (MAS) and general equilibrium marketing are proposed. An algorithm leading to the resource allocation decision involved in RCPSP has also been developed. And this algorithm can be used in the multi-project scheduling field as well.Finally, an illustration is given.

A new accelerating algorithm for multi-agent reinforcement learning

In multi-agent systems, joint-action must be employed to achieve cooperation because the evaluation of the behavior of an agent often depends on the other agents’ behaviors. However, joint-action reinforcement learning algorithms suffer the slow convergence rate because of the enormous learning space produced by joint-action. In this article, a prediction-based reinforcement learning algorithm is presented for multi-agent cooperation tasks, which demands all agents to learn predicting the probabilities of actions that other agents may execute. A multi-robot cooperation experiment is run to test the efficacy of the new algorithm, and the experiment results show that the new algorithm can achieve the cooperation policy much faster than the primitive reinforcement learning algorithm.

Distributed C-Means Algorithm for Big Data Image Segmentation on a Massively Parallel and Distributed Virtual Machine Based on Cooperative Mobile Agents

The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency.

Currency-based Iterative Multi-Agent Bidding Mechanism Based on Genetic Algorithm

This paper introduces a multi-agent system which i nt egrates process planning and production scheduling, in order to increase the fle xibility of manufacturing systems in coping with rapid changes in dynamic market and dealing with internal uncertainties such as machine breakdown or resources shortage. This system consists of various autonomous agents, each of which has t he capability of communicating with one another and making decisions based on it s knowledge and if necessary on information provided by other agents. Machine ag ents which represent the machines play an important role in the system in that t hey negotiate with each other to bid for jobs. An iterative bidding mechanism is proposed to facilitate the process of job assignment to machines and handle the negotiation between agents. This mechanism enables near optimal process plans a nd production schedules to be produced concurrently, so that dynamic changes in the market can be coped with at a minimum cost, and the utilisation of manufactu ring resources can be optimised. In addition, a currency scheme with currency-l ike metrics is proposed to encourage or prohibit machine agents to put forward t heir bids for the jobs announced. The values of the metrics are adjusted iterati vely so as to obtain an integrated plan and schedule which result in the minimum total production cost while satisfying products due dates. To deal with the optimisation problem, i.e. to what degree and how the currencies should be adj usted in each iteration, a genetic algorithm (GA) is developed. Comparisons are made between GA approach and simulated annealing (SA) optimisation technique.

航母航空保障作业中异质群体的动态路径规划算法

航母保障作业中路径规划任务存在着场景高动态性以及智能体的强异质性问题,传统的全局路径规划算法虽然能获得全局最优的结果,但无法适应高度动态变化的场景,且不能很好解决智能体的异质性所带来的安全性问题;当前的局部路径规划算法能够很好地解决智能体体型差异,但是异质群体行为控制表示难以统一表达。为了解决以上问题,提出了一种航母航空保障作业中的异质群体的动态路径规划算法。首先,将优化的全局和局部路径规划算法融合,解决航空保障作业场景的高动态性问题,根据动态环境信息及时调整路径,并充分考虑场景的高动态性给异质智能体带来的安全性问题。然后,该方法考虑异质智能体不同的行为特性,在局部碰撞避免过程中采用基于运动学特性的异质智能体行为控制模型。最后,以美国尼米兹号航母为例,使用UE4进行仿真实验,从路径长度、平滑度、安全性和避障能力等方面对该算法进行了评价。仿真实验结果表明,与其他路径规划算法相比,所提算法不仅可以生成航母甲板异质群体的安全路径,还能够满足异质群体在动态航空保障作业场景中的应用需求。

移动边缘计算下移动Agent的任务安全迁移策略

移动边缘计算中任务多在远离服务中心的边缘节点上执行,边缘节点的安全能力并不能得到保障,如何将任务迁移到安全可靠的边缘节点来执行是当前存在的一个问题。针对移动边缘计算下任务迁移中的安全风险问题,构建了适用于移动边缘计算环境的移动Agent安全风险计算模型,通过风险采集、风险处理和风险反馈综合评估边缘节点的抗风险值;并引入动态免疫算法,在任务计算和安全计算时间满足任务截止时间的前提下,实现任务迁移的安全能力最大化。仿真实验表明,相比于传统的任务迁移方案,该策略大大提高了移动边缘计算中任务迁移的抗风险能力,具有一定的鲁棒性。

基于随机博弈与A3C深度强化学习的网络防御策略优选

网络资源的有限性和攻防对抗的动态性导致最优防御策略难以选取,将深度强化学习引入攻防随机博弈建模领域,通过构建网络攻防actor策略网络和critic价值网络,结合随机博弈模型构建了网络攻防博弈决策模型总体结构,在此基础上引入异步优势演员评论家算法(asynchronous advantage actor-critic,A3C)智能体学习框架设计了防御策略选取算法;针对现有方法未考虑攻击方群体间的共谋攻击,引入群智能体性格特征,建立合作系数μ来刻画攻击者之间的合作对攻防策略收益的影响,进而得出对防御策略选取的影响,构建的博弈决策模型更符合攻防实际情况。实验结果表明,该方法的策略求解速度要优于现有方法,同时由于考虑了攻击合作关系,能够用于分析攻击者群体间合作关系对防御者决策的影响,防御策略选取更有针对性,期望防御收益更高。

Two-Agent Makespan Minimization Problem on Parallel Machines

A two-agent scheduling problem on parallel machines is considered in this paper. Our objective is to minimize the makespan for agent A, subject to an upper bound on the makespan for agent B. In this paper, we provide a new approximation algorithm called CLPT. On the one hand, we compare the performance between the CLPT algorithm and the optimal solution and find that the solution obtained by the CLPT algorithm is very close to the optimal solution. On the other hand, we design different experimental frameworks to compare the CLPT algorithm and the A-LS algorithm for a comprehensive performance evaluation. A large number of numerical simulation results show that the CLPT algorithm outperformed the A-LS algorithm.

基于Holon-Agent的无人机集群控制建模方法

无人机集群作战控制既包含任务级的协同规划,也包括平台级的状态控制,是一个极其复杂的多目标规划问题,但极具理论研究价值和实战运用意义。提出一种基于Holon-Agent的无人机集群控制建模方法,通过设计无人机、编队和集群多Holon-Agent的控制结构,并基于组件化建模思想构建Holon装备实体模型和多Agent行为控制模型,有效降低了无人机集群作战控制难度和仿真建模难度。其中,对多层Agent行为模型采用反应型和慎思型混合控制的方法实现,不仅充分发挥二者的优点,而且可按需构造不同智能化水平的无人机集群控制方式。通过仿真展示了基于Holon-Agent的大规模无人机集群抵近干扰作战控制过程,并对比了不同控制算法下的集群混合控制效果,验证了基于Holon-Agent的无人机集群混合控制的可靠性、鲁棒性、灵活性和智能性。

区域一致趋同的分布式负荷频率控制方法研究

随着可再生能源发电比例的上升,电力系统呈现源荷双重不确定性,对频率稳定产生不利影响。提出一种结合多智能体一致算法与动态面控制的分布式负荷频率控制方法,以增强电力系统的频率调节性能。首先,给出频率控制模型结构,并设计系统框架与分散式区域观测器;其次,考虑通信时滞的影响,设计出各区域控制器结构;最后,基于互联电力系统算例验证了方法的有效性与优越性。与传统控制方法相比,该控制方法改善了电力系统负荷频率控制性能,提高了区域间频率调节的一致性。

A MULTI-AGENT LOCAL-LEARNING ALGORITHM UNDER GROUP ENVIROMENT

In this paper,a local-learning algorithm for multi-agent is presented based on the fact that individual agent performs local perception and local interaction under group environment.As for in-dividual-learning,agent adopts greedy strategy to maximize its reward when interacting with envi-ronment.In group-learning,local interaction takes place between each two agents.A local-learning algorithm to choose and modify agents' actions is proposed to improve the traditional Q-learning algorithm,respectively in the situations of zero-sum games and general-sum games with unique equi-librium or multi-equilibrium.And this local-learning algorithm is proved to be convergent and the computation complexity is lower than the Nash-Q.Additionally,through grid-game test,it is indicated that by using this local-learning algorithm,the local behaviors of agents can spread to globe.

β成核聚丙烯/蒙脱土纳米复合材料的结构与力学性能

本文使用双螺杆挤出机制备了β成核聚丙烯(β-PP)/有机蒙脱土(OMMT)纳米复合材料。采用FTIR、XRD和SEM分析了复合材料的微观结构与形貌特征,考察了OMMT、β成核剂(β-NA)及增容剂用量对复合材料冲击强度和弯曲强度的影响,并对比研究了标准BP神经网络模型和LM-BP神经网络模型对力学性能的预测能力。结果表明,增容剂与OMMT表面形成了强相互作用,提高了复合体系的相容性,黏土片以插层结构分散在β-PP基体内。OMMT、β-NA及增容剂用量对复合材料的力学性能均产生了一定影响,其中添加30%增容剂时复合材料的冲击强度约为不添加时的4倍,而弯曲强度仅降低了28.39%。此外,与标准BP神经网络模型相比,LM-BP神经网络模型具有更快的收敛速度和更高的预测精度。该研究为优化PP基纳米复合材料的制备及力学性能预测提供了参考。

Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

面向供应链分销的多维空间Pareto边界自动谈判模型研究

随着电子商务的快速发展,自动谈判逐渐成为提升供应链系统效率的一种手段。为了优化多方参与的供应链分销谈判应用,本文将多边多属性谈判问题转化为多目标优化模型,采用改进的非支配遗传算法NSGA-Ⅲ计算多维空间的Pareto边界;然后,设计多线程谈判模型,将参与多方谈判的买卖各方拆解为多个双边谈判线程,分别在多维Pareto边界上进行谈判;进而,采用动态时间依赖策略(DTD),使Agent根据对方报价在Pareto边界上动态调整让步策略,快速达成协议。为验证模型的有效性,本文进行了大量模拟自动谈判实验。实验结果表明,所提出的改进算法和谈判流程优于领域最新研究成果,能有效提升多边多属性谈判效率,有助于多方达成共赢局面。

煤矿井下掘进机器人路径规划方法研究

针对煤矿非全断面巷道条件下掘进机器人移机难度大、效率低下等问题,分析了煤矿井下非结构化环境特征及掘进机器人运动特性,提出了基于深度强化学习的掘进机器人机身路径规划方法。利用深度相机将巷道环境实时重建,在虚拟环境中建立掘进机器人与巷道环境的碰撞检测模型,并使用层次包围盒法进行虚拟环境碰撞检测,形成巷道边界受限下的避障策略。考虑到掘进机器人形体大小且路径规划过程目标单一,在传统SAC算法的基础上引入后见经验回放技术,提出HER-SAC算法,该算法通过环境初始目标得到的轨迹扩展目标子集,以增加训练样本、提高训练速度。在此基础上,基于奖惩机制建立智能体,根据掘进机器人运动特性定义其状态空间与动作空间,在同一场景下分别使用3种算法对智能体进行训练,综合平均奖励值、最高奖励值、达到最高奖励值的步数以及鲁棒性4项性能指标进行对比分析。为进一步验证所提方法的可靠性,采用虚实结合的方式,通过调整目标位置设置2种实验场景进行掘进机器人的路径规划,并将传统SAC算法和HER-SAC算法的路径结果进行对比。结果表明:相较于PPO算法和SAC算法,HER-SAC算法收敛速度更快、综合性能达到最优;在2种实验场景下,HER-SAC算法相比传统SAC算法规划出的路径更加平滑、路径长度更短、路径终点与目标位置的误差在3.53 cm以内,能够有效地完成移机路径规划任务。该方法为煤矿掘进机器人的自主移机控制奠定了理论基础,为煤矿掘进设备自动化提供了新方法。

基于全同态加密优化的云数据隐私保护方法

为了提升云数据的安全性,改善数据加密时间长以及加密效果较差等问题,提出一种基于全同态加密优化的云数据隐私保护方法。通过生成对抗网络模型学习原始数据中的重要特征,确保合成的数据和初始数据之间具有较高的相似度,使其能够满足差分隐私特征。引入PKI对数据所有者和用户身份认证处理,完成密钥的产生和分发,根据差分隐私特征将用户所有者的数据划分为不同类型,获取用户访问特征向量。构建全同态加密机制,同时引入代理重加密机制,通过密钥转换完成数据加密处理,最终实现云数据全同态加密优化。实验对比结果表明,上述加密方案能够快速完成数据加密处理,有效确保数据的安全性。

一种基于进化Agent的遥感影像亚像元定位方法

遥感影像中存在着混合像元,软分类技术将这些像元按照一定的百分比划分为不同的地物类别,亚像元定位技术利用在每个混合像元中所获得的百分比信息,得到一个锐化后的分类影像。像元分解成不同的子像元,代表不同的地物类别成分。进化Agent技术结合一种空间邻域的假设关系,通过繁殖和扩散两种行为模式,分配给每一个亚像元一个确定的位置,从而达到定位的效果。利用合成影像和退化的真实影像进行实验,通过与传统的硬分类进行精度比较,证明进化Agent技术是一种简单易行的亚像元定位算法。