A Mutation Triggering Method for Genetic Algorithm to Solve Traveling Salesman Problem

This chapter reveals the effect of combining mutation operators in Genetic Algorithm. Though mutation is a very effective genetic operator to escape the local optima, it has an adverse effect on computation time. It is quite a challenge to say how much probability is appropriate for mutation. The major contribution of this chapter is to design a mutation triggering method by combining three mutation operators (Swap, Insertion and 2-Opt) and applying adaptive probability. To decide which mutation can be activated at a given generation, a decision-making method named Mutation Triggering Method is proposed. To measure the performance of the proposed method, the classical traveling salesman problem was considered. The experimental result shows that the proposed method was able to find a better solution (travel cost) than the other approaches. For computation time, the proposed strategy did as well as other mutation strategies. In conclusion, the combination of several mutation operators can ensure the benefits of diversity as well as the benefits of faster convergence.

[1]  K. Miettinen,et al.  Quasi-random initial population for genetic algorithms , 2004 .

[2]  Abid Hussain,et al.  Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator , 2017, Comput. Intell. Neurosci..

[3]  Jia Xu,et al.  Application of a Genetic Algorithm with Random Crossover and Dynamic Mutation on the Travelling Salesman Problem , 2018 .

[4]  Pedro A. Diaz-Gomez,et al.  Initial Population for Genetic Algorithms: A Metric Approach , 2007, GEM.

[5]  Dharm Raj Singh,et al.  Genetic Algorithm for Solving Multiple Traveling Salesmen Problem using a New Crossover and Population Generation , 2018, Computación y Sistemas.

[6]  Arindam Roy,et al.  A rough multi-objective genetic algorithm for uncertain constrained multi-objective solid travelling salesman problem , 2019 .

[7]  Deepti Mehrotra,et al.  Comparative review of selection techniques in genetic algorithm , 2015, 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE).

[8]  Zakir Hussain Ahmed.  A Hybrid Genetic Algorithm for the Bottleneck Traveling Salesman Problem , 2013, TECS.

[9]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[10]  Jaafar Abouchabaka,et al.  Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem , 2012, ArXiv.

[11]  Ayse T. Daloglu,et al.  An improved genetic algorithm with initial population strategy and self-adaptive member grouping , 2008 .

[12]  Kusum Deep,et al.  Combined Mutation Operators of Genetic Algorithm for the Travelling Salesman Problem , 2011, Int. J. Comb. Optim. Probl. Informatics.

[13]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[14]  N. Sathya,et al.  A Review of the Optimization Algorithms on Traveling Salesman Problem , 2015 .

[15]  Yang Liu,et al.  An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP , 2015 .

[16]  Novruz Allahverdi,et al.  Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms , 2011, Expert Syst. Appl..

[17]  Madan Lal Mittal,et al.  Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches , 2010 .

[18]  Ahmad B. A. Hassanat,et al.  Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness , 2018, Communications - Scientific letters of the University of Zilina.

[19]  Chin-Chih Chang,et al.  A 2-OPT WITH MUTATION OPERATOR TO THE TRAVELING SALESMAN PROBLEM , 2015 .

[20]  K. Katayama,et al.  The efficiency of hybrid mutation genetic algorithm for the travelling salesman problem , 2000 .