A memetic random-key genetic algorithm for a symmetric multi-objective traveling salesman problem

2008 ◽  
Vol 55 (2) ◽  
pp. 439-449 ◽  
Author(s):  
Funda Samanlioglu ◽  
William G. Ferrell ◽  
Mary E. Kurz
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Multi Objective

scholarly journals Algorithm for Multi- objective Traveling Salesman Problems based on Modified Transitive closure

2019 ◽  
Vol 9 (2) ◽  
pp. 2576-2581 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Transitive Closure ◽  
Traveling Salesman ◽  
Traveling Salesman Problems ◽  
Numerical Examples ◽  
Multi Objective ◽  
Closure Method ◽  
Closure Algorithm

One of the challenging facts of the Multi Objective Traveling Salesman Problem (MOTSP) is to find the best compromised solution. In this paper, we have proposed a modified transitive closure algorithm to solve MOTSP using Genetic Algorithm (GA). Modified Transitive Closure method generates all the initial solutions of each objective. By applying Genetic Algorithm (GA), compromised solutions are obtained. Numerical examples are provided to show the efficiency of the proposed algorithm for MOTSP

scholarly journals PENYELESAIAN MULTI TRAVELING SALESMAN PROBLEM DENGAN ALGORITMA GENETIKA

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
NI KADEK MAYULIANA ◽  
EKA N. KENCANA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Heuristic Algorithm ◽  
Minimum Distance ◽  
Processing Time ◽  
Traveling Salesman ◽  
Running Time ◽  
Shortest Route ◽  
Time Required

Genetic algorithm is a part of heuristic algorithm which can be applied to solve various computational problems. This work is directed to study the performance of the genetic algorithm (GA) to solve Multi Traveling Salesmen Problem (multi-TSP). GA is simulated to determine the shortest route for 5 to 10 salesmen who travelled 10 to 30 cities. The performance of this algorithm is studied based on the minimum distance and the processing time required for 10 repetitions for each of cities-salesmen combination. The result showed that the minimum distance and the processing time of the GA increase consistently whenever the number of cities to visit increase. In addition, different number of sales who visited certain number of cities proved significantly affect the running time of GA, but did not prove significantly affect the minimum distance.

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