scholarly journals Prim and Genetic Algorithms Performance in Determining Optimum Route on Graph

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Rusiadi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Traveling Salesman ◽  
Maximum Value ◽  
Optimum Route ◽  
Crossover And Mutation ◽  
Active Vertex

Performance is a process of assessment of the algorithm. Speed and security is the performance to be achieved in determining which algorithm is better to use. In determining the optimum route, there are two algorithms that can be used for comparison. The Genetic and Primary algorithms are two very popular algorithms for determining the optimum route on the graph. Prim can minimize circuit to avoid connected loop. Prim will determine the best route based on active vertex. This algorithm is especially useful when applied in a minimum spanning tree case. Genetics works with probability properties. Genetics cannot determine which route has the maximum value. However, genetics can determine the overall optimum route based on appropriate parameters. Each algorithm can be used for the case of the shortest path, minimum spanning tree or traveling salesman problem. The Prim algorithm is superior to the speed of Genetics. The strength of the Genetic algorithm lies in the number of generations and population generated as well as the selection, crossover and mutation processes as the resultant support. The disadvantage of the Genetic algorithm is spending to much time to get the desired result. Overall, the Prim algorithm has better performance than Genetic especially for a large number of vertices.

scholarly journals Comparative Study of Prim and Genetic Algorithms in Minimum Spanning Tree and Travelling Salesman Problem

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Andre Hasudungan Lubis
Keyword(s):  
Genetic Algorithm ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Maximum Value ◽  
Large Numbers ◽  
Crossover And Mutation ◽  
Better Than

Optimization is the essential thing in an algorithm. It can save the operational cost of an activity. At the Minimum Spanning Tree, the goal to be achieved is how all nodes are connected with the smallest weights. Several algorithms can calculate the use of weights in this graph. Genetic and Primary algorithms are two very popular algorithms for optimization. Prim calculates the weights based on the short-est distance from a graph. This algorithm eliminates the connected loop to minimize circuit. The nature of this algorithm is to trace all nodes to the smallest weights on a given graph. The genetic algorithm works by determining the random value as first initialization. This algorithm will perform selection, crossover, and mutation by the number of rounds specified. It is possible that this algorithm can not achieve the maximum value. The nature of the genetic algorithm is to work with probability. The results obtained are the most optimal results according to this algorithm. The results of this study indicate that the Prim is better than Genetics in determining the weights at the minimum spanning tree while Genetic algorithm is better for travelling salesman problem. Genetics will have maximum results when using large numbers of rotations and populations.

The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

2013 ◽  
Vol 411-414 ◽  
pp. 2013-2016 ◽  
Author(s):  
Guo Zhi Wen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Algorithm Animation ◽  
The Traveling Salesman Problem

The traveling salesman problem is analyzed with genetic algorithms. The best route map and tendency of optimal grade of 500 cities before the first mutation, best route map after 15 times of mutation and tendency of optimal grade of the final mutation are displayed with algorithm animation. The optimal grade is about 0.0455266 for the best route map before the first mutation, but is raised to about 0.058241 for the 15 times of mutation. It shows that through the improvements of algorithms and coding methods, the efficiency to solve the traveling problem can be raised with genetic algorithms.

Comparison of Heuristics for Resolving the Traveling Salesman Problem with Information Technology

2014 ◽  
Vol 886 ◽  
pp. 593-597 ◽  
Author(s):  
Wei Gong ◽  
Mei Li
Keyword(s):  
Information Technology ◽  
Traveling Salesman Problem ◽  
Spanning Tree ◽  
Nearest Neighbor ◽  
Minimum Spanning Tree ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Problem Class ◽  
The Traveling Salesman Problem ◽  
Random Insertion

Traveling Salesman Problem (Min TSP) is contained in the problem class NPO. It is NP-hard, means there is no efficient way to solve it. People have tried many kinds of algorithms with information technology. Thus in this paper we compare four heuristics, they are nearest neighbor, random insertion, minimum spanning tree and heuristics of Christofides. We dont try to find an optimal solution. We try to find approximated short trips via these heuristics and compare them.

scholarly journals Optimization Problems And Genetic Algorithms

2010 ◽  
Vol 14 (3) ◽  
Author(s):  
Jozef Zurada
Keyword(s):  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Evolution Theory ◽  
Optimization Task ◽  
Crossover And Mutation

This paper presents an application of genetic algorithms (GAs) to a well-known traveling salesman problem (TSP) which is a challenging optimization task. Using the techniques of selection, crossover, and mutation borrowed from the Darwin’s evolution theory, GAs were able to find the optimal solution after generating only 24 populations of solutions instead of exploring more than a million possible solutions.

scholarly journals Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP

1993 ◽  
Vol 1 (4) ◽  
pp. 313-333 ◽  
Author(s):  
Christine L. Valenzuela ◽  
Antonia J. Jones
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Search Problem ◽  
Genetic Approach ◽  
Solution Quality ◽  
Alternative Approach ◽  
The Traveling Salesman Problem

Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.

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.

scholarly journals A return to the traveling salesman model: a network branch and bound approach

2013 ◽  
Vol 16 (1) ◽  
pp. 52-63 ◽  
Author(s):  
Elias Munapo
Keyword(s):  
Linear Programming ◽  
Traveling Salesman Problem ◽  
Branch And Bound ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Traveling Salesman ◽  
Tree Model ◽  
The Traveling Salesman Problem ◽  
Assignment Models

This paper presents a network branch and bound approach for solving the traveling salesman problem. The problem is broken into sub-problems, each of which is solved as a minimum spanning tree model. This is easier to solve than either the linear programming-based or assignment models. 

scholarly journals Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Abid Hussain ◽  
Yousaf Shad Muhammad ◽  
M. Nauman Sajid ◽  
Ijaz Hussain ◽  
Alaa Mohamd Shoukry ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Crossover Operator ◽  
Mutation Operators ◽  
Path Representation ◽  
Hard Problems ◽  
The Traveling Salesman Problem ◽  
Survival Of The Fittest

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

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