Optimization Path programming using improved multigroup ant colony algorithms

Wen-Jong Chen, Li Jhen Jheng, Yan Ting Chen, Der-fa Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The main purpose of this chapter proposes an improved multigroup ant colony optimization (IMG-ACO) algorithm to improve the traditional ant colony optimization (TACO) algorithm and traditional multigroup ant colony optimization (MG-ACO) for dealing with the optimization path problem. The TACO and MG-ACO algorithms have exhibited good performance on searching the shortest path. But on the search space, it tends to suffer from premature convergence and fall into local optimal. In this study, the IMG-ACO algorithm utilizing traditional multigroup framework and mutation mechanism performs the virtual parallel optimization algorithm. Compared with the MG-ACO, the results show that the shortest path improved by about 11.5, 16.8, and 9.1% for 60, 90, and 120 nodes, respectively. This indicates that IMG-ACO can quickly obtain the optimal or nearly optimal solutions to the path programming problem.

Original languageEnglish
Title of host publicationIntelligent Technologies and Engineering Systems
Pages267-275
Number of pages9
DOIs
Publication statusPublished - 2013 Aug 8
Event2012 1st International Conference on Intelligent Technologies and Engineering Systems, ICITES 2012 - Changhua, Taiwan
Duration: 2012 Dec 132012 Dec 15

Publication series

NameLecture Notes in Electrical Engineering
Volume234 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

Other2012 1st International Conference on Intelligent Technologies and Engineering Systems, ICITES 2012
CountryTaiwan
CityChanghua
Period12-12-1312-12-15

Fingerprint

Ant colony optimization

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Chen, W-J., Jheng, L. J., Chen, Y. T., & Chen, D. (2013). Optimization Path programming using improved multigroup ant colony algorithms. In Intelligent Technologies and Engineering Systems (pp. 267-275). (Lecture Notes in Electrical Engineering; Vol. 234 LNEE). https://doi.org/10.1007/978-1-4614-6747-2_33
Chen, Wen-Jong ; Jheng, Li Jhen ; Chen, Yan Ting ; Chen, Der-fa. / Optimization Path programming using improved multigroup ant colony algorithms. Intelligent Technologies and Engineering Systems. 2013. pp. 267-275 (Lecture Notes in Electrical Engineering).
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abstract = "The main purpose of this chapter proposes an improved multigroup ant colony optimization (IMG-ACO) algorithm to improve the traditional ant colony optimization (TACO) algorithm and traditional multigroup ant colony optimization (MG-ACO) for dealing with the optimization path problem. The TACO and MG-ACO algorithms have exhibited good performance on searching the shortest path. But on the search space, it tends to suffer from premature convergence and fall into local optimal. In this study, the IMG-ACO algorithm utilizing traditional multigroup framework and mutation mechanism performs the virtual parallel optimization algorithm. Compared with the MG-ACO, the results show that the shortest path improved by about 11.5, 16.8, and 9.1{\%} for 60, 90, and 120 nodes, respectively. This indicates that IMG-ACO can quickly obtain the optimal or nearly optimal solutions to the path programming problem.",
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Chen, W-J, Jheng, LJ, Chen, YT & Chen, D 2013, Optimization Path programming using improved multigroup ant colony algorithms. in Intelligent Technologies and Engineering Systems. Lecture Notes in Electrical Engineering, vol. 234 LNEE, pp. 267-275, 2012 1st International Conference on Intelligent Technologies and Engineering Systems, ICITES 2012, Changhua, Taiwan, 12-12-13. https://doi.org/10.1007/978-1-4614-6747-2_33

Optimization Path programming using improved multigroup ant colony algorithms. / Chen, Wen-Jong; Jheng, Li Jhen; Chen, Yan Ting; Chen, Der-fa.

Intelligent Technologies and Engineering Systems. 2013. p. 267-275 (Lecture Notes in Electrical Engineering; Vol. 234 LNEE).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The main purpose of this chapter proposes an improved multigroup ant colony optimization (IMG-ACO) algorithm to improve the traditional ant colony optimization (TACO) algorithm and traditional multigroup ant colony optimization (MG-ACO) for dealing with the optimization path problem. The TACO and MG-ACO algorithms have exhibited good performance on searching the shortest path. But on the search space, it tends to suffer from premature convergence and fall into local optimal. In this study, the IMG-ACO algorithm utilizing traditional multigroup framework and mutation mechanism performs the virtual parallel optimization algorithm. Compared with the MG-ACO, the results show that the shortest path improved by about 11.5, 16.8, and 9.1% for 60, 90, and 120 nodes, respectively. This indicates that IMG-ACO can quickly obtain the optimal or nearly optimal solutions to the path programming problem.

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Chen W-J, Jheng LJ, Chen YT, Chen D. Optimization Path programming using improved multigroup ant colony algorithms. In Intelligent Technologies and Engineering Systems. 2013. p. 267-275. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-1-4614-6747-2_33