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Improved ant colony optimization algorithm for the traveling salesman problems 被引量:22
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作者 Rongwei Gan Qingshun Guo +1 位作者 Huiyou Chang Yang Yi 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第2期329-333,共5页
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 amo... 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. 展开更多
关键词 ant colony optimization heuristic algorithm scout ants path evaluation model traveling salesman problem.
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Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design 被引量:11
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作者 Zhao Baojiang Li Shiyong 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第3期603-610,共8页
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 s... 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. 展开更多
关键词 neuro-fuzzy controller ant colony algorithm function optimization genetic algorithm inverted pen-dulum system.
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Hybrid ant colony optimization for the resource-constrained project scheduling problem 被引量:10
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作者 Linyi Deng Yan Lin Ming Chen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第1期67-71,共5页
To solve the resource-constrained project scheduling problem (RCPSP), a hybrid ant colony optimization (HACO) approach is presented. To improve the quality of the schedules, the HACO is incorporated with an extend... To solve the resource-constrained project scheduling problem (RCPSP), a hybrid ant colony optimization (HACO) approach is presented. To improve the quality of the schedules, the HACO is incorporated with an extended double justification in which the activity splitting is applied to predict whether the schedule could be improved. The HACO is tested on the set of large benchmark problems from the project scheduling problem library (PSPLIB). The computational result shows that the proposed algo- rithm can improve the quality of the schedules efficiently. 展开更多
关键词 project scheduling double justification ant colony optimization activity splitting.
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Multi-objective optimization of operation loop recommendation for kill web 被引量:5
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作者 YANG Kewei XIA Boyuan +2 位作者 CHEN Gang YANG Zhiwei LI Minghao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第4期969-985,共17页
In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental ... In order to improve our military ’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e.,“web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model.Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem,we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition(MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition(MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives nondominated solution set for the multi-objective problem. Finally,compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume(HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making. 展开更多
关键词 multi-objective operation loop recommendation kill web ant colony evolutionary algorithm hyper volume(HV)
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Global path planning approach based on ant colony optimization algorithm 被引量:6
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作者 文志强 蔡自兴 《Journal of Central South University of Technology》 EI 2006年第6期707-712,共6页
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, concepti... Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted, the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path. 展开更多
关键词 mobile robot ant colony optimization global path planning PHEROMONE
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Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization 被引量:3
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作者 Chunfeng Wang Sanyang Liu Mingmin Zhu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第5期784-790,共7页
Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony opt... Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization(U-ACO-B) to solve the drawbacks of the ant colony optimization(ACO-B).In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm. 展开更多
关键词 Bayesian network structure learning ant colony optimization unconstrained optimization
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Multi-agent and ant colony optimization for ship integrated power system network reconfiguration 被引量:5
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作者 WANG Zheng HU Zhiyuan YANG Xuanfang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第2期489-496,共8页
Electric power is widely used as the main energy source of ship integrated power system(SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem.... Electric power is widely used as the main energy source of ship integrated power system(SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization(MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently. 展开更多
关键词 ship integrated power system(SIPS) multi-agent and ant colony optimization(MAACO) network reconfiguration ring grid fault recovery
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A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization 被引量:20
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作者 JI Jun-Zhong ZHANG Hong-Xun HU Ren-Bing LIU Chun-Nian 《自动化学报》 EI CSCD 北大核心 2009年第3期281-288,共8页
关键词 最优化 随机系统 自动化 BN
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Extraction of affine invariant features for shape recognition based on ant colony optimization 被引量:1
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作者 Yuxing Mao Ching Y. Suen Wei He 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第6期1003-1009,共7页
A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition.Firstly,the centroid distance and azimuth angle of each boundary point are computed.Th... A new approach to extraction of affine invariant features of contour image and matching strategy is proposed for shape recognition.Firstly,the centroid distance and azimuth angle of each boundary point are computed.Then,with a prior-defined angle interval,all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating noise.After that,the centroid distance ratios(CDRs) of any two opposite contour points to the barycenter are achieved as the representation of the shape,which will be invariant to affine transformation.Since the angles of contour points will change non-linearly among affine related images,the CDRs should be resampled and combined sequentially to build one-by-one matching pairs of the corresponding points.The core issue is how to determine the angle positions for sampling,which can be regarded as an optimization problem of path planning.An ant colony optimization(ACO)-based path planning model with some constraints is presented to address this problem.Finally,the Euclidean distance is adopted to evaluate the similarity of shape features in different images.The experimental results demonstrate the efficiency of the proposed method in shape recognition with translation,scaling,rotation and distortion. 展开更多
关键词 shape recognition affine transformation centroid distance ratio(CDR) ant colony optimization(ACO) path planning.
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Efficiency improvement of ant colony optimization in solving the moderate LTSP 被引量:1
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作者 Munan Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第6期1301-1309,共9页
In solving small- to medium-scale travelling salesman problems (TSPs) of both symmetric and asymmetric types, the traditional ant colony optimization (ACO) algorithm could work well, providing high accuracy and sa... In solving small- to medium-scale travelling salesman problems (TSPs) of both symmetric and asymmetric types, the traditional ant colony optimization (ACO) algorithm could work well, providing high accuracy and satisfactory efficiency. However, when the scale of the TSP increases, ACO, a heuristic algorithm, is greatly challenged with respect to accuracy and efficiency. A novel pheromone-trail updating strategy that moderately reduces the iteration time required in real optimization problem-solving is proposed. In comparison with the traditional strategy of the ACO in several experiments, the proposed strategy shows advantages in performance. Therefore, this strategy of pheromone-trail updating is proposed as a valuable approach that reduces the time-complexity and increases its efficiency with less iteration time in real optimization applications. Moreover, this strategy is especially applicable in solving the moderate large-scale TSPs based on ACO. 展开更多
关键词 ant colony optimization (ACO) travelling salesmanproblem (TSP) time-complexity of algorithm pheromone-trail up-dating.
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Improved multi-objective artificial bee colony algorithm for optimal power flow problem 被引量:1
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作者 马连博 胡琨元 +1 位作者 朱云龙 陈瀚宁 《Journal of Central South University》 SCIE EI CAS 2014年第11期4220-4227,共8页
The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting obj... The artificial bee colony(ABC) algorithm is improved to construct a hybrid multi-objective ABC algorithm, called HMOABC, for resolving optimal power flow(OPF) problem by simultaneously optimizing three conflicting objectives of OPF, instead of transforming multi-objective functions into a single objective function. The main idea of HMOABC is to extend original ABC algorithm to multi-objective and cooperative mode by combining the Pareto dominance and divide-and-conquer approach. HMOABC is then used in the 30-bus IEEE test system for solving the OPF problem considering the cost, loss, and emission impacts. The simulation results show that the HMOABC is superior to other algorithms in terms of optimization accuracy and computation robustness. 展开更多
关键词 cooperative artificial colony algorithm optimal power flow multi-objective optimization
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Bayesian-based ant colony optimization algorithm for edge detection
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作者 YU Yongbin ZHONG Yuanjingyang +6 位作者 FENG Xiao WANG Xiangxiang FAVOUR Ekong ZHOU Chen CHENG Man WANG Hao WANG Jingya 《Journal of Systems Engineering and Electronics》 2025年第4期892-902,共11页
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t... Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task. 展开更多
关键词 ant colony optimization(ACO) Bayesian algorithm edge detection transfer function.
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Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm 被引量:26
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作者 Wang Yanxia Qian Longjun Guo Zhi Ma Lifeng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第5期939-944,共6页
A weapon target assignment (WTA) model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the we... A weapon target assignment (WTA) model satisfying expected damage probabilities with an ant colony algorithm is proposed. In order to save armament resource and attack the targets effectively, the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted. The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result. Ant colony algorithm has been successfully used in many fields, especially in combination optimization. The ant colony algorithm for this WTA problem is described by analyzing path selection, pheromone update, and tabu table update. The effectiveness of the model and the algorithm is demonstrated with an example. 展开更多
关键词 weapon target assignment ant colony algorithm optimization.
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Adaptive optimization of agile organization of command and control resource 被引量:8
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作者 Yang Chunhui Liu Junxian +1 位作者 Chen Honghui Luo Xueshan 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第3期558-564,共7页
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 for... 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. 展开更多
关键词 command and control organization adaptive optimization of organization dynamic-window-search ant colony optimization 3-phase organizational design.
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Solving algorithm for TA optimization model based on ACO-SA 被引量:4
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作者 Jun Wang Xiaoguang Gao Yongwen Zhu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2011年第4期628-639,共12页
An ant colony optimization (ACO)-simulated annealing (SA)-based algorithm is developed for the target assignment problem (TAP) in the air defense (AD) command and control (C2) system of surface to air missi... An ant colony optimization (ACO)-simulated annealing (SA)-based algorithm is developed for the target assignment problem (TAP) in the air defense (AD) command and control (C2) system of surface to air missile (SAM) tactical unit. The accomplishment process of target assignment (TA) task is analyzed. A firing advantage degree (FAD) concept of fire unit (FU) intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat. 展开更多
关键词 target assignment (TA) optimization ant colony optimization (ACO) algorithm simulated annealing (SA) algorithm hybrid optimization strategy.
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Novel method based on ant colony opti mization for solving ill-conditioned linear systems of equations 被引量:1
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作者 段海滨 王道波 朱家强 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2005年第3期606-610,共5页
A novel method based on ant colony optimization (ACO), algorithm for solving the ill-conditioned linear systems of equations is proposed. ACO is a parallelized bionic optimization algorithm which is inspired from th... A novel method based on ant colony optimization (ACO), algorithm for solving the ill-conditioned linear systems of equations is proposed. ACO is a parallelized bionic optimization algorithm which is inspired from the behavior of real ants. ACO algorithm is first introduced, a kind of positive feedback mechanism is adopted in ACO. Then, the solu- tion problem of linear systems of equations was reformulated as an unconstrained optimization problem for solution by an ACID algorithm. Finally, the ACID with other traditional methods is applied to solve a kind of multi-dimensional Hilbert ill-conditioned linear equations. The numerical results demonstrate that ACO is effective, robust and recommendable in solving ill-conditioned linear systems of equations. 展开更多
关键词 ill-conditioned linear systems of equations ant colony optimization condition number optimization.
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Research of Rural Power Network Reactive Power Optimization Based on Improved ACOA
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作者 YU Qian ZHAO Yulin WANG Xintao 《Journal of Northeast Agricultural University(English Edition)》 CAS 2010年第3期48-52,共5页
In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this stud... In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects: one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county's distribution network showed that the improved ACOA was practicable. 展开更多
关键词 rural power network reactive power optimization ant colony optimization algorithm local search strategy pheromone mutation and re-initialization strategy
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双机器人的任务分配和协同作业算法研究
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作者 李铁军 赵博言 +2 位作者 刘今越 贾晓辉 唐春瑞 《控制工程》 北大核心 2025年第4期577-585,共9页
针对双机器人难以实现合理的任务分配和协同作业的问题,提出了一种基于工作量平衡机制与主从协同蚁群优化算法完成双机器人的任务分配和协同作业的方法。首先,基于任务点集合建立不平衡任务指派模型,任务分配阶段通过迭代路径规划算法... 针对双机器人难以实现合理的任务分配和协同作业的问题,提出了一种基于工作量平衡机制与主从协同蚁群优化算法完成双机器人的任务分配和协同作业的方法。首先,基于任务点集合建立不平衡任务指派模型,任务分配阶段通过迭代路径规划算法平衡两机器人的工作量。然后,通过主从协同蚁群优化算法解算机器人之间避免干涉且保持工作量最小的多目标协同作业优化模型。最后,结合钢筋绑扎场景展开实验,实验结果表明,所提方法可以在两机器人之间实现合理的任务分配,减少二者的工作差异量,使其高效地完成钢筋绑扎作业,并且可以有效避免机器人在作业过程中发生干涉。 展开更多
关键词 双机器人 任务分配 主从协同 蚁群优化算法
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基于ARIMA与GGACO算法的ETL任务调度机制研究
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作者 周金治 刘艺涵 吴斌 《控制工程》 北大核心 2025年第2期208-215,共8页
随着抽取-转换-加载(extraction-transformation-loading,ETL)系统的ETL任务量增多,任务复杂度和波动性也随之提升,现有的ETL任务调度机制难以满足调度需求,如时间片轮转法受限于弹性调度能力弱、效率低下等缺点。为研究如何提升ETL任... 随着抽取-转换-加载(extraction-transformation-loading,ETL)系统的ETL任务量增多,任务复杂度和波动性也随之提升,现有的ETL任务调度机制难以满足调度需求,如时间片轮转法受限于弹性调度能力弱、效率低下等缺点。为研究如何提升ETL任务调度机制的弹性调度能力以及执行效率,提出了一种基于整合移动平均自回归(autoregressive integrated moving average,ARIMA)模型与贪心-遗传-蚁群优化(greedy-genetic-ant colony optimization,GGACO)算法的ETL任务调度机制。初期,建立ARIMA模型并弹性地结合贪心算法计算初始解;中期,利用遗传算法的全局快收敛的特性结合初始解圈定最优解的大致范围;最后,利用蚁群优化算法的局部快速收敛性进行最优解搜索。实验结果表明:该调度机制能够弹性地指导任务调度尽可能地找到最优解,减少任务的执行时间,以及尽可能实现更高效的负载均衡。 展开更多
关键词 弹性调度 ARIMA 贪心算法 遗传算法 蚁群优化算法
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考虑飞机除冰任务的除冰车路径规划模型研究
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作者 徐一旻 王台玉冰 +2 位作者 吕伟 刘鸣秋 吴佳莉 《中国安全生产科学技术》 北大核心 2025年第8期181-188,共8页
为应对冻雨天气下机场除冰作业中车辆调度效率低、动态避障能力不足及多约束条件耦合优化困难等问题,提出1种基于混合蚁群算法的机场除冰车辆路径规划与动态调度优化模型。首先通过栅格化建模技术,将机场CAD地图转化为离散网格空间,综... 为应对冻雨天气下机场除冰作业中车辆调度效率低、动态避障能力不足及多约束条件耦合优化困难等问题,提出1种基于混合蚁群算法的机场除冰车辆路径规划与动态调度优化模型。首先通过栅格化建模技术,将机场CAD地图转化为离散网格空间,综合考虑障碍物动态分布、航班起飞优先级、除冰液有效时间窗、车辆容量限制等约束,构建多目标优化函数。其次,基于混合蚁群算法的全局寻优能力与A^(*)算法的局部路径优化特性,实现复杂环境下路径规划与避障的协同控制。实验基于真实机场脱敏地图构建仿真场景,划分20个区域并标注所有停机位坐标,验证了模型的有效性和鲁棒性。研究结果表明:该模型在确保航班时刻表约束的前提下,总行驶距离减少68%,航班延误时间减少90%,有效规避障碍物膨胀区边界的同时能动态调整多车辆协作路径。研究结果可为冻雨天气下机场除冰作业提供兼顾全局最优性与动态适应性的解决方案。 展开更多
关键词 路径规划 机场除冰车辆 动态调度 混合蚁群算法 多目标优化
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