Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness

https://doi.org/10.26552/com.C.2020.3.128-139

  • Esra'a Alkafaween
  • Ahmad B. A. Hassanat
Keywords: knowledge-based mutation, inversion mutation, slide mutation, RGIBNNM, SBM

Abstract

Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution. The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA.

Various mutation operators exist to solve hard combinatorial problems such as the TSP. In this paper, we propose a hybrid mutation operator called "IRGIBNNM", this mutation is a combination of two existing mutations; a knowledgebased mutation, and a random-based mutation. We also improve the existing “select best mutation” strategy using the proposed mutation.

We conducted several experiments on twelve benchmark Symmetric traveling salesman problem (STSP) instances. The results of our experiments show the efficiency of the proposed mutation, particularly when we use it with some other mutations.

Author Biographies

Esra'a Alkafaween

IT Department, Mutah University, Karak, Jordan

Ahmad B. A. Hassanat

IT Department, Mutah University, Karak, Jordan and Computer Science Department, Community College, University of Tabuk, Saudi Arabia and Industrial Innovation and Robotics Center, University of Tabuk, Saudi Arabia


Published
2020-07-08
How to Cite
Esra’a Alkafaween, & Ahmad B. A. Hassanat. (2020). Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness. Communications - Scientific Letters of the University of Zilina, 22(3), 128-139. https://doi.org/10.26552/com.C.2020.3.128-139
Section
Management Science and Informatics in Transport