There are many factors that affect the performance of the evolutionary and memetic algorithms. One of these factors is the proper selection of the initial population, as it represents a very important criterion contributing to the convergence speed. Selecting a conveniently preprocessed initial population definitely increases the convergence speed and thus accelerates the probability of steering the search towards better regions in the search space, hence, avoiding premature convergence towards a local optimum. In this paper, we propose a new method for generating the initial individual candidate solution called Circle Group Heuristic (CGH) for Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is built with aid of a simple Genetic Algorithm (GA). CGH has been tested for several benchmark reference data of the Travelling Salesman Problem (TSP). The practical results show that CGH gives better tours compared with other well-known heuristic tour construction methods.

The creation of the initial population is an essential part of the population based evolutionary algorithms. An appropriate initial population could lead to much faster convergence speed; in contrast, an inappropriate initial population could even cause getting stuck in a local optimum. In this paper, we will propose a new efficient heuristic method to create initial individuals for the Traveling Salesman Problem (TSP), which we will call Circle Group Heuristic (CGH). The results show that CGH creates better tours compared with other well-known heuristic tour construction methods.