@inproceedings{3bfb55aaa4f1418ebf4bc58d8b3b7623,

title = "Candidate set strategies for ant colony optimisation",

abstract = "Ant Colony Optimisation based solvers systematically scan the set of possible solution elements before choosing a particular one. Hence, the computational time required for each step of the algorithm can be large. One way to overcome this is to limit the number of element choices to a sensible subset, or candidate set. This paper describes some novel generic candidate set strategies and tests these on the travelling salesman and car sequencing problems. The results show that the use of candidate sets helps to find competitive solutions to the test problems in a relatively short amount of time.",

author = "Marcus Randall and James Montgomery",

year = "2002",

month = jan,

day = "1",

doi = "10.1007/3-540-45724-0_22",

language = "English",

isbn = "9783540457244",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag London Ltd.",

pages = "243--249",

editor = "{Dorigo }, Marco and {di Caro}, Gianni and Michael Sampels",

booktitle = "Ant Algorithms - 3rd International Workshop, ANTS 2002, Proceedings",

address = "Italy",

note = "3rd International Workshop on Ant Algorithms, ANTS 2002 ; Conference date: 12-09-2002 Through 14-09-2002",

}