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Applicability of Genetic and Ant Algorithms in Highway Alignment and Rail Transit Station Location Optimization

Applicability of Genetic and Ant Algorithms in Highway Alignment and Rail Transit Station Location Optimization

Sutapa Samanta, Manoj K. Jha
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 24
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781466613812|DOI: 10.4018/joris.2012010102
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MLA

Samanta, Sutapa, and Manoj K. Jha. "Applicability of Genetic and Ant Algorithms in Highway Alignment and Rail Transit Station Location Optimization." IJORIS vol.3, no.1 2012: pp.13-36. http://doi.org/10.4018/joris.2012010102

APA

Samanta, S. & Jha, M. K. (2012). Applicability of Genetic and Ant Algorithms in Highway Alignment and Rail Transit Station Location Optimization. International Journal of Operations Research and Information Systems (IJORIS), 3(1), 13-36. http://doi.org/10.4018/joris.2012010102

Chicago

Samanta, Sutapa, and Manoj K. Jha. "Applicability of Genetic and Ant Algorithms in Highway Alignment and Rail Transit Station Location Optimization," International Journal of Operations Research and Information Systems (IJORIS) 3, no.1: 13-36. http://doi.org/10.4018/joris.2012010102

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Abstract

The emergence of artificial intelligence (AI)-based optimization heuristics like genetic and ant algorithms is useful in solving many complex transportation location optimization problems. The suitability of such algorithms depends on the nature of the problem to be solved. This study examines the suitability of genetic and ant algorithms in two distinct and complex transportation problems: (1) highway alignment optimization and (2) rail transit station location optimization. A comparative study of the two algorithms is presented in terms of the quality of results. In addition, Ant algorithms (AAs) have been modified to search in a global space for both problems, a significant departure from traditional AA application in local search problems. It is observed that for the two optimization problems both algorithms give almost similar solutions. However, the ant algorithm has the inherent limitation of being effective only in discrete search problems. When applied to continuous search spaces ant algorithm requires the space to be sufficiently discretized. On the other hand, genetic algorithms can be applied to both discrete and continues spaces with reasonable confidence. The application of AA in global search seems promising and opens up the possibility of its application in other complex optimization problems.

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