Details

Autor(en) / Beteiligte

• Alkafaween, Esra'a
• Hassanat, Ahmad B. A

Titel

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

Ist Teil von

• Komunikácie : vedecké listy Žilinskej univerzity = Communications : scientific letters of the University of Žilina, 2020-07, Vol.22 (3), p.128-139

Ort / Verlag

University of Žilina

Erscheinungsjahr

2020

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.