An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

Yong Deng,Deyun Zhou,Yang Liu

doi:10.1155/2015/212794

Abstract

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on thek-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process isk-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

Highlights

Traveling salesman problem (TSP) is a well-known NP-hard problem in many real-world applications, such as job-shop scheduling and VLSI routing [1, 2]
The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time
Many methods have been developed for solving TSP, including exact algorithms and approximate algorithms

Summary

Introduction

Traveling salesman problem (TSP) is a well-known NP-hard problem in many real-world applications, such as job-shop scheduling and VLSI routing [1, 2]. The approximate algorithms, especially many bioinspired algorithms [4,5,6,7,8,9], can obtain accepted solutions for many NPhard problems with (relatively) short running time. These approaches are usually very simple, like Lin-Kernigan [10], colony optimization (ACO) [11], and so on [12, 13]. All of them are efficient approaches in most of the problems, but ACO, for example, is not suitable for large-scale TSP because of its computational cost O(mn2), in which m is the ant numbers and n is the number of cities

Methods

Results

Conclusion