NDLI: A-nearness Ant Colony System with Adaptive Strategies for the Traveling Salesman Problem

Content Provider	Semantic Scholar
Author	Lv, Jin-Qiu You, Xiao-Ming Liu, Sheng
Copyright Year	2014
Abstract	On account of ant colony algorithm easy to fall into local optimum, this paper presents an improved ant colony optimization called α-AACS and reports its performance. At first, we provide an concise description of the original ant colony system(ACS) and introduce α-nearness based on the minimum 1-tree for ACS’s disadvantage, which better reflects the chances of a given link being a member of an optimal tour. Then, we improve α-nearness by computing a lower bound and propose other adaptations for ACS. Finally, we conduct a fair competition between our algorithm and others. The results clearly show that α-AACS has a better global searching ability in finding the best solutions, which indicates that α-AACS is an effective approach for solving the traveling salesman problem.
Starting Page	15
Ending Page	24
Page Count	10
File Format	PDF HTM / HTML
DOI	10.5121/ijfcst.2014.4502
Volume Number	4
Alternate Webpage(s)	http://wireilla.com/papers/ijfcst/V4N5/4514ijfcst02.pdf
Alternate Webpage(s)	https://doi.org/10.5121/ijfcst.2014.4502
Journal	FOCS 2014
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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