Ant colony algorithm for satellite control resource scheduling problem

Zhang, Zhaojun; Hu, Funian; Zhang, Na

doi:10.1007/s10489-018-1144-z

Cited by 57 publications

(24 citation statements)

References 25 publications

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“…Transmission lines bear the heavy responsibility of transmitting electrical energy and are the most extensively distributed component of the power grid [1,2]. With the gradual construction of the power grid, the total length of long-distance, large-capacity AC and DC transmission lines of various voltage levels are gradually increasing.…”

Section: Introductionmentioning

confidence: 99%

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Liu

2021

Security and Communication Networks

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After a transmission line fails, quickly and accurately find the fault point and deal with it, which is of great significance to maintaining the normal operation of the power system. Aiming at the problems of low accuracy of traditional traveling wave fault location methods and many affected factors, this paper relies on distributed traveling wave monitoring points arranged on transmission lines to study methods to improve the accuracy of traveling wave fault location on transmission lines. First, when a line fails, a traveling wave signal that moves to both ends will be generated and transmitted along the transmission line. We use the Radon transform algorithm to process the traveling wave signal. Then, this paper uses ant colony algorithm to analyze and verify the location and extent of transmission line faults and then optimizes high-precision collection and processing. Finally, the simulation distance measurement is carried out on double-terminal transmission lines and multiterminal transmission lines (T-shaped lines) with branches. The results show that, for double-ended transmission lines, the algorithm increases the speed of matrix calculations and at the same time makes the fault location error of the transmission grid still maintain the improved effect.

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Section: Introductionmentioning

confidence: 99%

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Liu

2021

Security and Communication Networks

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“…To overcome the deficiencies in the static mission clustering, Wu et al [19] presented an adaptive simulated annealing-based scheduling algorithm integrated with a dynamic mission clustering strategy for satellites observation scheduling. Zhang et al [20], [21] proposed an ant colony optimization (ACO) to effectively plan various control resources for ensuring the normal operation of satellites. Yu et al [22] proposed an improved cooperation-oriented ACO to solve the scheduling problem of aerial multi-target staring surveillance with multi-satellites.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Autonomous Scheduling Approach for Multiple Earth Observation Satellites With Intensive Missions

Guo

Wang

et al. 2021

IEEE Access

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Autonomous mission scheduling of multiple earth observation satellites (multi-EOSs) is considered as a complicated combinatorial optimization problem, which requires simultaneous consideration of imaging needs, resource constraints (electricity and memory) and possible emergencies. However, EOS resources are extremely scarce relative to intensive mission observation demands and most of the existing algorithms seldom consider emergencies. To address these challenges, this paper proposes a complete multi-EOSs scheduling scheme composed of two coupling stages, including mission pre-planning and mission replanning. We aim to obtain the optimal scheduling scheme for each EOS at the same time by maximizing the observation profits and balancing the resource consumption of each EOS. In this study, the roles of solar energy and ground stations in multi-EOSs mission scheduling are also considered. In the first stage, based on the cooperation and competition mechanism as well as the dynamic adjustment approach, an evolutionary ant colony optimization (EACO) method is developed to obtain the optimal solution for multi-EOSs preplanning. In the second stage, using the results produced by EACO, we propose an interactive replanning approach to replan the missions that cannot be performed by faulty EOS in the event of unexpected accidents. Finally, several target scenarios are designed and numerical experiments are performed to show that the proposed algorithm presents better performance for large-scale multi-EOSs missions than other state-of-the-art algorithms.INDEX TERMS Multiple earth observation satellites, autonomous mission scheduling, evolutionary ant colony optimization, dynamic adjustment approach, interactive replanning approach

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“…Considering task merging for EOS observation, Wu et al [41] proposed a simulated annealing algorithm based on the adaptive neighborhood operators. Moreover, metaheuristics for EOS scheduling also include tabu search algorithms [20,26,34,35,46], genetic algorithms [23,31,39,42], ant colony algorithms [10,24,40,45], local search algorithms [21,22] and simulated annealing algorithms [18,20].…”

Section: Introductionmentioning

confidence: 99%

Expectation and SAA Models and Algorithms for Scheduling of Multiple Earth Observation Satellites Under the Impact of Clouds

Wang

Demeulemeester²,

et al. 2020

IEEE Systems Journal

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In the explosively increased number of applications of earth observation satellites (EOSs), scheduling is a significative issue to satisfy more requests and obtain a high observation efficiency. This paper investigates the scheduling of multiple EOSs under the impact of clouds. Firstly, we formulate the presences of clouds as stochastic events, and propose an expectation model. Afterwards, for the first time, a branch-and-price algorithm based on Dantzig-Wolfe decomposition is devised to solve the model optimally and efficiently. In order to obtain initial columns for column generation, a dynamic programming algorithm and a heuristic algorithm are suggested, respectively. Furthermore, we discuss the impact of clouds in the case of joint probabilities, and establish a sample average approximation (SAA) model accordingly. With respect to the expectation model, numerical experiment results demonstrate the relative dominance of the proposed branch-and-price algorithm in terms of solution time compared to CPLEX. In addition, the solution of the proposed SAA model is proven to be more robust than that of the expectation model.

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Ant colony algorithm for satellite control resource scheduling problem

Cited by 57 publications

References 25 publications

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence

A Cooperative Autonomous Scheduling Approach for Multiple Earth Observation Satellites With Intensive Missions

Expectation and SAA Models and Algorithms for Scheduling of Multiple Earth Observation Satellites Under the Impact of Clouds

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