An improved genetic algorithm with a local optimization strategy and an extra mutation level for solving traveling salesman problem
This is an incremental improvement for solving NP-complete optimization problems like TSP.
The paper tackled the Traveling Salesman Problem by proposing an improved genetic algorithm with local optimization strategies, resulting in better paths than conventional GA within acceptable computation time.
The Traveling salesman problem (TSP) is proved to be NP-complete in most cases. The genetic algorithm (GA) is one of the most useful algorithms for solving this problem. In this paper a conventional GA is compared with an improved hybrid GA in solving TSP. The improved or hybrid GA consist of conventional GA and two local optimization strategies. The first strategy is extracting all sequential groups including four cities of samples and changing the two central cities with each other. The second local optimization strategy is similar to an extra mutation process. In this step with a low probability a sample is selected. In this sample two random cities are defined and the path between these cities is reversed. The computation results show that the proposed method also finds better paths than the conventional GA within an acceptable computation time.