A new quantum-inspired genetic algorithm for solving the travelling salesman problem

Abstract

This paper presents anew algorithm for solving the Travelling Salesmm Problem (TSP). The TSP is one of the most known combinatorial optimisation problems. It is about finding the shortest Homilronian cycle relating N cities. The algorithm is inspired from both genetic aigorithms and quantum compirting fields. It exrends the standard genedic dgorifhms by combining them to some concepts and principles provided from quantum computing field szich as giiantum bit, states superposition and intetfterence. The obfained resulls fram fhe application of the proposed algorithm on some insfumes of TSP are signi5canfly bettw than those provided by standard genetic algorithms.

Quantum-inspired evolutionary algorithms: a survey and empirical study

“…It uses crossover and mutation operators to replace the bQIEAo migration operators. According to the bQIEAcm reported in Li et al (2005a), Xu et al (2005), Meshoul et al (2005aMeshoul et al ( , 2005b, Wang et al (2005c), Yang et al (2004aYang et al ( , 2004bYang et al ( , 2005; Talbi et al (2004aTalbi et al ( , 2004bTalbi et al ( , 2004c, Li and Zhuang (2002), Abdesslem et al (2006), Yang and Jiao (2003), Guo et al (2007), Yang and Ding (2007), Shu (2007), Wei et al (2008), Ding et al (2008), Zhao et al (2009), the pseudocode algorithm can be summarized as shown in Fig. 6.…”

The development and application of metaheuristics for problems in graph theory: a computational study

“…quantum logic gates), quantum-inspired computing could help quantum hardware platforms to be feasible. The increased performance of Quantum-inspired Genetic Algorithms with respect to classical Genetic Algorithms has been recently demonstrated for some classical combinatorial optimization problems, such as the travelling salesman problem (Talbi et al, 2004).…”

An Improved Greedy Genetic Algorithm for Solving Travelling Salesman Problem

“…The main principle of GA [3] Selection. Selection is to evaluate each individual and keeps only the fittest ones among them.…”

“…These problems require very usually a colossal amount of computing resources to be solved efficiently because the solutions space that should be explored gains exponentially when the size of the problem increases [3].…”