Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness
This work addresses optimization in combinatorial problems like TSP for researchers and practitioners, but it is incremental as it builds on existing mutations and strategies.
The paper tackled the Traveling Salesman Problem by proposing a hybrid mutation operator (IRGIBNNM) combining knowledge-based and random-based mutations, and improving a selection strategy, with experiments on twelve benchmark instances showing efficiency, particularly when used with other mutations.
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 knowledge-based 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. Keyword: Knowledge-based mutation, Inversion mutation, Slide mutation, RGIBNNM, SBM.