Ant Colony Optimization Algorithm and Artificial Immune System Applied to a Robot Route

被引：3

作者：

Ribeiro, J. M. S. ^{[1
]}

Silva, M. F. ^{[1
]}

Santos, M. F. ^{[2
]}

Vidal, V. F. ^{[1
]}

Honorio, L. M. ^{[1
]}

Silva, L. A. Z. ^{[1
]}

Rezende, H. B. ^{[1
]}

Santos Neto, A. F. ^{[2
]}

Mercorelli, P. ^{[3
]}

Pancoti, A. A. N. ^{[1
]}

机构：

[1] Univ Fed Juiz de Fora, Juiz de Fora, MG, Brazil

[2] CEFET MG, Jose Peres 558, Leopoldina, MG, Brazil

[3] Leuphana Univ Lueneburg, Volgershall 1, D-21339 Luneburg, Germany

来源：

2019 20TH INTERNATIONAL CARPATHIAN CONTROL CONFERENCE (ICCC) | 2019年

关键词：

Heuristics Techniques; Ant Colony Optimization; Artificial Immune System; Robot; HYBRID;

D O I：

10.1109/carpathiancc.2019.8765910

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：675 / 680

页数：6

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