Path Planning of Mobile Robot Using Traveling Salesman Problem Modeled for Genetic Algorithm

2019 ◽  
pp. 297-306
Author(s):  
Roni Jose George ◽  
P. Supriya
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Traveling Salesman Problem ◽  
Traveling Salesman

scholarly journals Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-31 ◽  
Author(s):  
Giovanni Giardini ◽  
Tamás Kalmár-Nagy
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Optimal Strategy ◽  
Numerical Experiments ◽  
Solution Method ◽  
Autonomous Systems ◽  
Traveling Salesman ◽  
Optimal Solutions

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

scholarly journals ON SAMPLING BASED METHODS FOR THE DUBINS TRAVELING SALESMAN PROBLEM WITH NEIGHBORHOODS

2015 ◽  
Vol 2 (2) ◽  
pp. 57-61
Author(s):  
Petr Váňa ◽  
Jan Faigl
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Solution Quality ◽  
Goal Region ◽  
Dubins Vehicle ◽  
Proposed Modification ◽  
Dubins Traveling Salesman Problem

In this paper, we address the problem of path planning to visit a set of regions by Dubins vehicle, which is also known as the Dubins Traveling Salesman Problem Neighborhoods (DTSPN). We propose a modification of the existing sampling-based approach to determine increasing number of samples per goal region and thus improve the solution quality if a more computational time is available. The proposed modification of the sampling-based algorithm has been compared with performance of existing approaches for the DTSPN and results of the quality of the found solutions and the required computational time are presented in the paper.

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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