NDLI: Group mosquito host-seeking algorithm

Content Provider	Springer Nature Link
Author	Feng, Xiang Liu, Xiaoting Yu, Huiqun
Copyright Year	2015
Abstract	The host-seeking behavior of mosquitoes is very interesting. This paper is motivated by the following general observation on mosquito groups and their host-seeking behavior in nature: (1) Mosquitoes’ behavior has possession of the parallelism, openness, local interactivity and self-organization. (2) Mosquito groups seek host very fast. (3) The host-seeking behavior is similar to the producer-scrounger process, which assumes that group members search either for “finding” (producer) or for “joining” (scrounger) opportunities. It stimulates us to extend a mosquito system model in nature to group mosquito host-seeking model (GMHSM) and algorithm (GMHSA) for intelligent computing. In this paper, we propose GMHS approach and show how to use it. By GMHSM, the TSP is transformed into the kinematics and dynamics of mosquito groups host-seeking process. The properties of GMHSM and GMHSA, including the correctness, convergence and stability, have been discussed in this paper. The GMHS approach has many advantages in terms of multiple objective optimization, large-scale distributed parallel optimization, effectiveness of problem-solving and suitability for complex environment. Via simulations, we test the GMHS approach and compare it with other state-of-art algorithms.
Starting Page	665
Ending Page	686
Page Count	22
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	44
Issue Number	3
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2015-11-04
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Group Mosquito Host-Seeking Model (GMHSM) and algorithm (GMHSA) Leader decision Traveling Salesman Problem (TSP) Distributed and parallel algorithm Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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