A multi-objective-based non-stationary UAV assignment model for constraints handling using PSO

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A multi-objective-based non-stationary UAV assignment model for constraints handling using PSO

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ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents

Shanghai, China

SESSION: Full papers table of contents

Pages 459-466

Year of Publication: 2009

ISBN:978-1-60558-326-6

Authors

Feng Pan	Beijing Institute of Technology, Beijing, China
Guanghui Wang	Beijing Institute of Technology, Beijing, China
Yang Liu	Beijing Institute of Technology, Beijing, China

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.

REFERENCES

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