DocumentCode
2709738
Title
Improving the Performance of Genetic Algorithm in Capacitated Vehicle Routing Problem using Self Imposed Constraints
Author
Ursani, Ziauddin ; Sarker, Ruhul ; Abbass, Hussein A.
Author_Institution
Sch. of Inf. Technol. & Electr. Eng., New South Wales Univ., Canberra, ACT
fYear
2007
fDate
1-5 April 2007
Firstpage
220
Lastpage
225
Abstract
The capacitated vehicle routing problem (CVRP) is a well known member of the family of NP hard problems. In the past few decades, a number of heuristics was introduced to solve this problem but no heuristic can claim to work well in all possible scenarios. In the literature, genetic algorithm (GA) even lags behind the other heuristics. In this paper, we reveal some of the reasons for the inferior performance of GA, and propose a number of mechanisms to improve its performance. A number of test problems are solved to demonstrate the usefulness of the algorithm.
Keywords
computational complexity; constraint theory; genetic algorithms; transportation; vehicles; NP hard problems; capacitated vehicle routing problem; genetic algorithm; self imposed constraints; Australia; Computational intelligence; Genetic algorithms; Information technology; Intelligent vehicles; NP-hard problem; Performance analysis; Processor scheduling; Routing; Vehicle driving;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence in Scheduling, 2007. SCIS '07. IEEE Symposium on
Conference_Location
Honolulu, HI
Print_ISBN
1-4244-0704-4
Type
conf
DOI
10.1109/SCIS.2007.367693
Filename
4218620
Link To Document