A novel parallel object-tracking behavior algorithm based on dynamics for data clustering

Feng, Xiang; Lai, Zhaolin; Yu, Huiqun

doi:10.1007/s00500-019-04058-4

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…A wide variety of approaches have been reported for solving the problem of rescue task allocation and achieved meaningful results. Traditional methods of addressing task allocation problems include behavior-based algorithms [21], marketbased algorithms [22], linear planning methods [23], and intelligent optimization algorithms [24][25][26]. Technically, the behavior-based algorithm has the advantages of realtime, fault-tolerance, and robustness, but unable to obtain the global optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Geng

Chen

Nguyen

et al. 2021

Complex Intell. Syst.

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This paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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