Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm

Haibo Liu, Yang Liao, Changting Shi () and Jing Shen
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Haibo Liu: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Yang Liao: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Changting Shi: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Jing Shen: College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China

Future Internet, 2024, vol. 16, issue 4, 1-20

Abstract: The objective of task allocation in unmanned systems is to complete tasks at minimal costs. However, the current algorithms employed for coordinating multiple unmanned systems in task allocation tasks frequently converge to local optima, thus impeding the identification of the best solutions. To address these challenges, this study builds upon the sheep flock optimization algorithm (SFOA) by preserving individuals eliminated during the iterative process within a prior knowledge set, which is continuously updated. During the reproduction phase of the algorithm, this prior knowledge is utilized to guide the generation of new individuals, preventing their rapid reconvergence to local optima. This approach aids in reducing the frequency at which the algorithm converges to local optima, continually steering the algorithm towards the global optimum and thereby enhancing the efficiency of task allocation. Finally, various task scenarios are presented to evaluate the performances of various algorithms. The results show that the algorithm proposed in this paper is more likely than other algorithms to escape from local optima and find the global optimum.

Keywords: multi-unmanned systems; sheep flock optimization algorithm; prior knowledge; task allocation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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