Comparison of Ant Colony Optimization and Artificial Bee Colony Algorithms for Solving Electronic Support Search Dwell Scheduling Problem

Erkut, Okan; Hardalaç, Fırat

doi:10.1109/uyms54260.2021.9659666

Cited by 2 publications

(7 citation statements)

References 24 publications

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“…The adaptive dwell scheduling algorithms can be further divided into the ones based on the heuristic method [5][6][7][8][9][10][11][12][13][14] and the ones based on the intelligent method [15][16][17][18][19][20][21][22][23][24][25][26][27]. A dwell scheduling algorithm based on time pointer is proposed in ref.…”

Section: Introductionmentioning

confidence: 99%

“…In ref. [18], two electronic support search dwell scheduling algorithms based on ant colony algorithm and artificial bee colony algorithm respectively are proposed, where the performance and the computational complexity of these two algorithms are compared. In refs.…”

Section: Introductionmentioning

confidence: 99%

“…All the dwell scheduling algorithms based on the intelligent algorithm in refs. [15][16][17][18][19][20][21][22][23][24][25] treat dwell tasks as nonpreemptive tasks. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…[9,10] can shorten the bias between the actual execution time and the desired execution time, but dwell tasks are more likely to be dropped. Secondly, although algorithms based on intelligent algorithms [15][16][17][18][19][20][21][22][23][24][25][26][27] can achieve different criteria of scheduling, they are not real-time dwell scheduling algorithms, which means that they do not apply to the actual radar systems. Last but not the least, most algorithms treat dwell tasks as non-preemptive tasks, which limited the performance of the dwell scheduling algorithm [5][6][7][8][9][10][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, although algorithms based on intelligent algorithms [15][16][17][18][19][20][21][22][23][24][25][26][27] can achieve different criteria of scheduling, they are not real-time dwell scheduling algorithms, which means that they do not apply to the actual radar systems. Last but not the least, most algorithms treat dwell tasks as non-preemptive tasks, which limited the performance of the dwell scheduling algorithm [5][6][7][8][9][10][15][16][17][18][19][20][21][22][23][24][25]. Although the pulse interleaving techniques are introduced in some algorithms, they are not general or reasonable, such as the ones in refs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy

Cheng

Heng

2022

IET Radar Sonar & Navi

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The effective dwell scheduling algorithm must be studied to make full use of the radar system's limited resources. In this study, a real‐time dwell scheduling in phased array radar is investigated, where the optimisation problem model that takes the importance, urgency and desired execution time criteria into consideration is built. A real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy is proposed to solve this problem. Based on the scheduled task pointed by the time pointer, when it is not interleaved with the previously scheduled tasks, two previously interleaved blocks are backtracked, and the actual execution time of tasks in these two blocks are adjusted. In order to guarantee real‐time scheduling, the concept of the virtual dwell task is introduced to reduce the dimension of the original execution time optimisation problem first, then it is converted into two convex optimisation problems and their analytical solutions are deduced. Simulation results demonstrate that the proposed algorithm can realise real‐time dwell scheduling, and it has the lowest Task Drop Ratio, the highest Hit Value Ratio, Time Utilisation Ratio and low Average Time Shifting Ration compared with conventional and up‐to‐date algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…All the dwell scheduling algorithms based on the intelligent algorithm in refs. [15][16][17][18][19][20][21][22][23][24][25] treat dwell tasks as nonpreemptive tasks. In ref.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy

Cheng

Heng

2022

IET Radar Sonar & Navi

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Real-time path planning based on improved artificial potential field method

Wang

2023

ACE

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Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.

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Comparison of Ant Colony Optimization and Artificial Bee Colony Algorithms for Solving Electronic Support Search Dwell Scheduling Problem

Cited by 2 publications

References 24 publications

Real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy

Real‐time dwell scheduling algorithm for phased array radar based on a backtracking strategy

Real-time path planning based on improved artificial potential field method

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