Extended Abstract
Introduction
The problem of locating bank branches is classified asNP-Hard problem which can possibly be solved only in exponential time by the increase in the number of banks and the large number of customers, especially when the location model includes various datasets, several objectives and constraints. As a consequence, we need to use heuristic methods to solve these types of problems. Also, since majority of data and analyses applied in the locating problems are spatial; GIScience’s abilities should be employed besides optimization methods.
Nowadays, to perform particular financial tasks, bank customers often need to be present at their bank. For the sake of its customers, a bank should increase its branches in the city to attract more customers in the race with competing banks. However, establishing new branches is too expensive and banks prefer to carry out an optimal location finding procedure. Such procedures should consider many criteria and objectives including spatial data of customers, new and existing bank branches as well as the level of attraction of banks. Customers often select a bank that is closer to them, has better services or financial records and also consider other human or physical factors. Hence, planning to increase the number of customers for a new branch of a bank considering spatial criteria and various other objectives appears necessary.
 
Materials & Methods
This paper determines the location of bank branches. Finding an optimum site for branches depends on many factors and these problems are known as NP-hard problems. Despite being approximate methods, meta-heuristic algorithms seem suitable tools for solving NP-hard problems. In this paper, Grey Wolf Optimizer (GWO), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Cultural Algorithms (CA) and Invasive Weed Optimization (IWO) are applied for finding the best location for bank branches. From marketing point of view, the aim is to attract more customers while the number of attracted people to a new branch should be acceptable. The new methods have capability to find the optimum location for new branches. The location of a new branch should be as far away as possible from branches of the same bank. The other condition is that the total number of customers for the new branch should not be less than a specified number, while the new branch should not attract customers of old branches of the same bank. To fulfill this propose, a part of the city of Tabriz was selected for implementation.The assumptions for the defined problem can be expressed as the following statements:
a)We consider four different banks (Melli, Mellat, Sepah and Mehr) in our study area.
b)Population density (of people over 15 years of age) is available at the building block level.
c)Banks have infinite capacity for accepting customers.
d)Each customer refers to only one bank.
e)New bank branches should have maximum distance from the branches of the same bank, so that, it attracts minimum number of customers from branches of the same bank.
 
Conclusion
  To evaluate the quality and accuracy of the algorithms, several iterations are performed. The results of statistical and final tests indicate that the accuracy and convergence speed of Invasive Weed Optimization are more than other Algorithms in finding optimal location of bank branches.