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Erschienen in: Cluster Computing 4/2019

07.03.2018

Optimization and application of artificial intelligence routing algorithm

verfasst von: Qiang Meng, Jianjun Zhang

Erschienen in: Cluster Computing | Sonderheft 4/2019

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Abstract

In order to optimize the artificial intelligence routing algorithm, combined with the calculation of the direction of the vector space model, three strategies are proposed to optimize the A* algorithm. The A* algorithm is widely used in the fields of GIS system and game path finding system. However, with the expansion of the scale of the search map, its performance consumption has increased exponentially. First of all, the first step of A* algorithm is to move towards the target direction by using directional factors, so that the intermediate route process will approach the shortest path as soon as possible. Secondly, the direction factor is used to ensure that the path finding of A* algorithm is the priority point in the direction of the target. Finally, the fault tolerance process is carried out. When the direction factor is guided to the “dead end”, it can be traced back, to ensure that the shortest path can be found at the end. The results show that the A * optimization algorithm is effective. The performance of A * optimization algorithm is about 20–50% higher than the traditional A * algorithm. The best case reached 88.6%. Therefore, the proposed optimization method improves the efficiency of the algorithm and reduces the performance consumption of the algorithm.

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Literatur
1.
Zurück zum Zitat Kuila, P., Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng. Appl. Artif. Intell. 33(1), 127–140 (2014)CrossRef Kuila, P., Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng. Appl. Artif. Intell. 33(1), 127–140 (2014)CrossRef
2.
Zurück zum Zitat Ilin, V., Simić, D., Tepić, J., et al.: A survey of hybrid artificial intelligence algorithms for dynamic vehicle routing problem. Lecture Notes Comput. Sci. 9121, 644–655 (2015)CrossRef Ilin, V., Simić, D., Tepić, J., et al.: A survey of hybrid artificial intelligence algorithms for dynamic vehicle routing problem. Lecture Notes Comput. Sci. 9121, 644–655 (2015)CrossRef
3.
Zurück zum Zitat Azharuddin, M.D., Kuila, P., Jana, P.K.: Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Comput. Electr. Eng. 41 (C), 177–190 (2015)CrossRef Azharuddin, M.D., Kuila, P., Jana, P.K.: Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Comput. Electr. Eng. 41 (C), 177–190 (2015)CrossRef
4.
Zurück zum Zitat Gupta, S.K., Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wirel. Pers. Commun. 83(3), 1–21 (2015) Gupta, S.K., Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wirel. Pers. Commun. 83(3), 1–21 (2015)
5.
Zurück zum Zitat Hong, S., Han, K.H.: Tree-based routing algorithms on wireless sensor networks: survey. J. Syst. Inf. Technol. 17(2), 216–221 (2014) Hong, S., Han, K.H.: Tree-based routing algorithms on wireless sensor networks: survey. J. Syst. Inf. Technol. 17(2), 216–221 (2014)
6.
Zurück zum Zitat Al-Rawi, H.A.A., Ming, A.N., Yau, K.L.A.: Application of reinforcement learning to routing in distributed wireless networks: a review. Artif. Intell. Rev. 43(3), 381–416 (2015)CrossRef Al-Rawi, H.A.A., Ming, A.N., Yau, K.L.A.: Application of reinforcement learning to routing in distributed wireless networks: a review. Artif. Intell. Rev. 43(3), 381–416 (2015)CrossRef
7.
Zurück zum Zitat Saltouros, M.P., Verentziotis, E.A., Markaki, M.E., et al.: An efficient hybrid genetic algorithm for finding (near-) optimal steiner trees: an approach to routing of multipoint connections. Int. J. Comput. Appl. 22(3), 159–165 (2015) Saltouros, M.P., Verentziotis, E.A., Markaki, M.E., et al.: An efficient hybrid genetic algorithm for finding (near-) optimal steiner trees: an approach to routing of multipoint connections. Int. J. Comput. Appl. 22(3), 159–165 (2015)
8.
Zurück zum Zitat Marinakis, Y., Marinaki, M., Migdalas, A.: A hybrid clonal selection algorithm for the location routing problem with stochastic demands. Ann. Math. Artif. Intell. 76(1–2), 121–142 (2016)MathSciNetCrossRef Marinakis, Y., Marinaki, M., Migdalas, A.: A hybrid clonal selection algorithm for the location routing problem with stochastic demands. Ann. Math. Artif. Intell. 76(1–2), 121–142 (2016)MathSciNetCrossRef
9.
Zurück zum Zitat Fan, Y.H.: Line probe routing algorithm implementation for SOC. Front. Artif. Intell. Appl. 274, 315–320 (2015) Fan, Y.H.: Line probe routing algorithm implementation for SOC. Front. Artif. Intell. Appl. 274, 315–320 (2015)
10.
Zurück zum Zitat Maghayreh, E.A., Al-Haija, S.A., Aljawarneh, S., et al.: BeesAnts: a new nature-inspired routing algorithm. Int. J. Commun. Netw. Distrib. Syst. 10(1), 83–97 (2013)CrossRef Maghayreh, E.A., Al-Haija, S.A., Aljawarneh, S., et al.: BeesAnts: a new nature-inspired routing algorithm. Int. J. Commun. Netw. Distrib. Syst. 10(1), 83–97 (2013)CrossRef
11.
Zurück zum Zitat Kumaravel, K., Marimuthu, A.: An efficient multi-path routing algorithm based on hybrid firefly algorithm for wireless mesh networks. Res. J. Appl. Sci. Eng. Technol. 10(2), 159–168 (2015) Kumaravel, K., Marimuthu, A.: An efficient multi-path routing algorithm based on hybrid firefly algorithm for wireless mesh networks. Res. J. Appl. Sci. Eng. Technol. 10(2), 159–168 (2015)
12.
Zurück zum Zitat Wang, X., Cheng, H., Huang, M.: Multi-robot navigation based QoS routing in self-organizing networks. Eng. Appl. Artif. Intell. 26(1), 262–272 (2013)CrossRef Wang, X., Cheng, H., Huang, M.: Multi-robot navigation based QoS routing in self-organizing networks. Eng. Appl. Artif. Intell. 26(1), 262–272 (2013)CrossRef
13.
Zurück zum Zitat Buyukyildiz, M., Tezel, G., Yilmaz, V.: Estimation of the change in lake water level by artificial intelligence methods. Water Resour. Manage 28(13), 4747–4763 (2014)CrossRef Buyukyildiz, M., Tezel, G., Yilmaz, V.: Estimation of the change in lake water level by artificial intelligence methods. Water Resour. Manage 28(13), 4747–4763 (2014)CrossRef
14.
Zurück zum Zitat Akinwande, O.J., Bi, H., Gelenbe, E.: Managing crowds in hazards with dynamic grouping. Access IEEE 3(4), 1060–1070 (2015)CrossRef Akinwande, O.J., Bi, H., Gelenbe, E.: Managing crowds in hazards with dynamic grouping. Access IEEE 3(4), 1060–1070 (2015)CrossRef
15.
Zurück zum Zitat Dalfard, V.M.: A new intelligence algorithm for determination of shortest path for dynamic guidance of vehicles based on service level criterion. Int. J. Oper. Res. 19(4), 497–512 (2014)MathSciNetCrossRef Dalfard, V.M.: A new intelligence algorithm for determination of shortest path for dynamic guidance of vehicles based on service level criterion. Int. J. Oper. Res. 19(4), 497–512 (2014)MathSciNetCrossRef
Metadaten
Titel
Optimization and application of artificial intelligence routing algorithm
verfasst von
Qiang Meng
Jianjun Zhang
Publikationsdatum
07.03.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-1963-z

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