Communications - Scientific Letters of the University of Zilina 2020, 22(3):128-139 | DOI: 10.26552/com.C.2020.3.128-139

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

Esra'a Alkafaween¹, Ahmad B. A. Hassanat²

¹ IT Department, Mutah University, Karak, Jordan

² 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

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.

Keywords: knowledge-based mutation; inversion mutation; slide mutation; RGIBNNM; SBM

Received: August 18, 2019; Accepted: December 5, 2019; Published: July 8, 2020  Show citation