Abstract
The objective of this work is to present a solution to a multiple-objective optimization problem using genetic algorithms (GA). Generally, the objectives (minimizing cost, maximizing performance, reducing carbon footprints, maximizing profit) are conflicting for multiple-objective problems, hindering concurrent optimization of each objective. A bi-objective traditional combinatorial optimization of Travelling Salesman Problem is undertaken named as the Multi-Objective Travelling Salesman Problem (MTSP). The two objectives are minimization of the distance travelled by the salesman and minimization of the time taken to travel. The purpose of this paper is to model the problem as a single objective optimization problem using the weighted sum method of modeling the objective function and using a Genetic Algorithm to see how the distance and time values change with the changes in weights assigned to the two objectives. The probability of mutation, the initial population and the number of generations have been varied to study its effect on the fitness value.
Recommended Citation
R. S. Beed et al., "A Study of the Genetic Algorithm Parameters for Solving Multi-Objective Travelling Salesman Problem," Proceedings - 2017 International Conference on Information Technology, ICIT 2017, pp. 23 - 29, article no. 8423877, Institute of Electrical and Electronics Engineers, Jul 2018.
The definitive version is available at https://doi.org/10.1109/ICIT.2017.49
Department(s)
Computer Science
Keywords and Phrases
Crossover; Fitness; Genetic Algorithms (GA); Initial Population; Mutation; Tournament Selection; Travelling Salesman Problem (TSP)
International Standard Book Number (ISBN)
978-153862924-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
31 Jul 2018
