A genetic algorithm to solve 3D traveling salesman problem with initial population based on a GRASP algorithm

2017 ◽  
Vol 17 ◽  
pp. S1-S10 ◽  
Author(s):  
Sebastian Meneses ◽  
Rony Cueva ◽  
Manuel Tupia ◽  
Miguel Guanira
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Population Based ◽  
Traveling Salesman ◽  
Initial Population

Picking Route Optimization of Automated Warehouse Based on Improved Genetic Algorithms

2013 ◽  
Vol 411-414 ◽  
pp. 2694-2697
Author(s):  
Pei Guang Wang ◽  
Xing Min Qi ◽  
Xiao Ping Zong ◽  
Ling Ling Zhu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Optimization Problem ◽  
Evolution Operator ◽  
Order Picking ◽  
Traveling Salesman ◽  
Route Optimization ◽  
Initial Population

In order to improve the efficiency of automated warehouse, the order-picking task of the fixed shelve was researched and analysed. The picking mathematical model of automated warehouse was established and attributed to the classical traveling salesman problem (TSP) model. At the same time, using an improved genetic algorithms(improved GAs) solved the optimization problem. Firstly, the initial population of the algorithm was optimized, and then a 'reverse evolution operator' was introduced in the improved genetic algorithms because of the lack of local optimization ability of genetic algorithm. Results of experiments verify that the method can acquire satisfying the demands of the route picking and optimization of speed.

Genetic Algorithm for Optimizing Traveling Salesman Problems with Time Windows (TSP-TW)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Juwairiah Juwairiah ◽  
Dicky Pratama ◽  
Heru Cahya Rustamaji ◽  
Herry Sofyan ◽  
Dessyanto Boedi Prasetyo
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Time Windows ◽  
Time Variable ◽  
Traveling Salesman ◽  
Initial Population ◽  
Genetic Operators ◽  
Traveling Salesman Problems ◽  
Exchange Method ◽  
The Traveling Salesman Problem

The concept of Traveling Salesman Problem (TSP) used in the discussion of this paper is the Traveling Salesman Problem with Time Windows (TSP-TW), where the time variable considered is the time of availability of attractions for tourists to visit. The algorithm used for optimizing the solution of Traveling Salesman Problem with Time Windows (TSP-TW) is a genetic algorithm. The search for a solution for determining the best route begins with the formation of an initial population that contains a collection of individuals. Each individual has a combination of different tourist sequence. Then it is processed by genetic operators, namely crossover with Partially Mapped Crossover (PMX) method, mutation using reciprocal exchange method, and selection using ranked-based fitness method. The research method used is GRAPPLE. Based on tests conducted, the optimal generation size results obtained in solving the TSP-TW problem on the tourist route in the Province of DIY using genetic algorithms is 700, population size is 40, and the combination of crossover rate and mutation rate is 0.70 and 0.30 There is a tolerance time of 5 seconds between the process of requesting distance and travel time and the process of forming a tourist route for the genetic algorithm process.

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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