scholarly journals An Improved Genetic Algorithm with a Local Optimization Strategy and an Extra Mutation Level for Solving Traveling Salesman Problem

2014 ◽  
Vol 4 (4) ◽  
pp. 47-53 ◽  
Author(s):  
Keivan Borna ◽  
Vahid Haji Hashemi
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Local Optimization ◽  
Traveling Salesman ◽  
Optimization Strategy ◽  
Improved Genetic Algorithm

Application of an Improved Genetic Algorithm with the Search Space Compression in TSP

2013 ◽  
Vol 765-767 ◽  
pp. 687-689
Author(s):  
Yi Song ◽  
Ni Ni Wei
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Random Search ◽  
Search Space ◽  
Traveling Salesman ◽  
Improved Genetic Algorithm ◽  
Space Compression ◽  
Accelerate Convergence ◽  
The Traveling Salesman Problem ◽  
Exponential Relation

The Traveling Salesman Problem is a combinatorial optimization problem, the problem has been shown to belong to the NPC problem, the possible solution of Traveling Salesman Problem and the scale of the cities have the exponential relation, so the more bigger of the scale. In this paper, improve the search process of the genetic algorithm by introducing the idea is to compress the search space. The simulation results show that for solving the TSP, the algorithm can quickly obtain multiple high-quality solutions. It can reduce the blindness of random search and accelerate convergence of the algorithm.

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.

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