Ant colony optimization algorithm and artificial immune system applied to a robot route
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Ant colony optimization algorithm and artificial immune system applied to a robot route. / Ribeiro, J. M.S.; Silva, M. F.; Santos, M. F. et al.
Proceedings of the 2019 20th International Carpathian Control Conference, ICCC 2019. Hrsg. / Andrzej Kot; Agata Nawrocka. Piscataway : IEEE - Institute of Electrical and Electronics Engineers Inc., 2019. 8765910 (Proceedings of the 2019 20th International Carpathian Control Conference, ICCC 2019).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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RIS
TY - CHAP
T1 - Ant colony optimization algorithm and artificial immune system applied to a robot route
AU - Ribeiro, J. M.S.
AU - Silva, M. F.
AU - Santos, M. F.
AU - Vidal, V. F.
AU - Honorio, L. M.
AU - Silva, L. A.Z.
AU - Rezende, H. B.
AU - Santos Neto, A. F.
AU - Mercorelli, P.
AU - Pancoti, A. A.N.
N1 - Conference code: 20
PY - 2019/5/1
Y1 - 2019/5/1
N2 - This Article aims to introduce two meta-heuristics techniques: Ant Colony Optimization (ACO) and Artificial Immune System (AIS) to find the best route for a robot. The ACO is an algorithm based on the ant food search process, and the AIS is inspired by the defending mechanism of the human organism. In order to illustrate and compare the potential of these techniques, this paper applies both techniques in a problem of determining the shortest possible route for a robot without hitting any obstacles in three different maps. According to the tests, the ACO shows better results regarding the number of iterations to reach the global optimum, while the AIS shows better results when it comes to the processing time. From the result, it can be seen that the ACO found a solution to all maps demonstrating it is an excellent choice for this problem type.
AB - This Article aims to introduce two meta-heuristics techniques: Ant Colony Optimization (ACO) and Artificial Immune System (AIS) to find the best route for a robot. The ACO is an algorithm based on the ant food search process, and the AIS is inspired by the defending mechanism of the human organism. In order to illustrate and compare the potential of these techniques, this paper applies both techniques in a problem of determining the shortest possible route for a robot without hitting any obstacles in three different maps. According to the tests, the ACO shows better results regarding the number of iterations to reach the global optimum, while the AIS shows better results when it comes to the processing time. From the result, it can be seen that the ACO found a solution to all maps demonstrating it is an excellent choice for this problem type.
KW - Ant Colony Optimization
KW - Artificial Immune System
KW - Heuristics Techniques
KW - Robot
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85069847968&partnerID=8YFLogxK
U2 - 10.1109/CarpathianCC.2019.8765910
DO - 10.1109/CarpathianCC.2019.8765910
M3 - Article in conference proceedings
SN - 978-1-7281-0703-5
T3 - Proceedings of the 2019 20th International Carpathian Control Conference, ICCC 2019
BT - Proceedings of the 2019 20th International Carpathian Control Conference, ICCC 2019
A2 - Kot, Andrzej
A2 - Nawrocka, Agata
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 20st International Carpathian Control Conference - ICCC 2019
Y2 - 26 May 2019 through 29 May 2019
ER -