scholarly journals Perbandingan Algoritma Cheapest Insertion Heuristics dan Pemrograman Dinamis untuk Penyelesaian Traveling Salesman Problem

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Anggar Titis Prayitno
Keyword(s):  
Dynamic Programming ◽  
Traveling Salesman Problem ◽  
Calculation Result ◽  
Efficient Algorithm ◽  
Time Complexity ◽  
Traveling Salesman ◽  
Algorithm Efficiency ◽  
Heuristics Algorithm ◽  
Complexity Algorithm

ABSTRACT  Traveling Salesman Problem (TSP) is one of combinatorics optimation problem to find the possible shorthest path that can be obtained if a  salesman visit each city exactly once and return to the starting city. The shorthest path searching can be done by Cheapest Insertion Heuristics algorithm and Dynamic Programming. Each algorithm has different efficiency to find shorthest path. Algorithm efficiency is determined based on time complexity. Algorithm wich has the smallest time complexity is the most efficient algorithm. Based on the calculation result, the time complexity of Cheapest Insertion Heuristics algorithm is and Dynamic Programming is .  Therefore, for  Cheapest Insertion Heuristics Algorithm is more efficient algorithm than Dynamic Programming in TSP solving. Keywords : Traveling Salesman Problem, Cheapest Insertion Heuristics  Algorithm, Dynamic Programming, and Algorithm time complexity.

scholarly journals Solving Large-Scale TSP Using a Fast Wedging Insertion Partitioning Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zuoyong Xiang ◽  
Zhenyu Chen ◽  
Xingyu Gao ◽  
Xinjun Wang ◽  
Fangchun Di ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Time Complexity ◽  
Large Scale ◽  
Construction Method ◽  
Traveling Salesman ◽  
Experimental Results ◽  
Insertion Method ◽  
Symmetric Traveling Salesman Problem ◽  
Traditional Construction

A new partitioning method, called Wedging Insertion, is proposed for solving large-scale symmetric Traveling Salesman Problem (TSP). The idea of our proposed algorithm is to cut a TSP tour into four segments by nodes’ coordinate (not by rectangle, such as Strip, FRP, and Karp). Each node is located in one of their segments, which excludes four particular nodes, and each segment does not twist with other segments. After the partitioning process, this algorithm utilizes traditional construction method, that is, the insertion method, for each segment to improve the quality of tour, and then connects the starting node and the ending node of each segment to obtain the complete tour. In order to test the performance of our proposed algorithm, we conduct the experiments on various TSPLIB instances. The experimental results show that our proposed algorithm in this paper is more efficient for solving large-scale TSPs. Specifically, our approach is able to obviously reduce the time complexity for running the algorithm; meanwhile, it will lose only about 10% of the algorithm’s performance.

A Genetic Algorithm with the Mixed Heuristics for Traveling Salesman Problem

2015 ◽  
Vol 14 (01) ◽  
pp. 1550003 ◽  
Author(s):  
Yong Wang
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Discrete Optimization ◽  
Time Complexity ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Optimization Process ◽  
Hamiltonian Cycles ◽  
Improved Genetic Algorithm ◽  
Discrete Optimization Problems

Traveling salesman problem (TSP) is one of well-known discrete optimization problems. The genetic algorithm is improved with the mixed heuristics to resolve TSP. The first heuristics is the four vertices and three lines inequality, which is applied to the 4-vertex paths to generate the shorter Hamiltonian cycles (HC). The second local heuristics is executed to reverse the i-vertex paths with more than two vertices, which also generates the shorter HCs. It is necessary that the two heuristics coordinate with each other in the optimization process. The time complexity of the first and second heuristics are O(n) and O(n3), respectively. The two heuristics are merged into the original genetic algorithm. The computation results show that the improved genetic algorithm with the mixed heuristics can find better solutions than the original GA does under the same conditions.

scholarly journals Dynamic programming approaches for the traveling salesman problem with drone

Networks ◽  
2018 ◽  
Vol 72 (4) ◽  
pp. 528-542 ◽  
Author(s):  
Paul Bouman ◽  
Niels Agatz ◽  
Marie Schmidt
Keyword(s):  
Dynamic Programming ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem
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