Education is one of the most vital sectors of any nation’s development. Site selection for Education Centers (EC) like schools, colleges, and coaching centers can be a very complex process. Various parameters like population, literacy rate, property cost, etc. have to be considered while selecting a site. Though deterministic approaches employed for site selection have been proven to give the best possible solution, they fail to work on large datasets. Recently metaheuristics have become very popular for solving optimization problems. This paper presents two integrated approaches, Fuzzy Genetic Algorithm for EC site selection (FGA-ECSS) and Fuzzy Binary Particle Swarm Optimization for EC site selection (FBPSO-ECSS) for choosing sites optimally. To evaluate the effectiveness of the two approaches, FGA-ECSS and FBPSO-ECSS have been compared with each other as well as with Genetic Algorithm and Binary Particle Swarm Optimization. The results obtained from the proposed solutions are promising and indicate that they can be used for solving such optimization problems.

教育是任何国家发展中最重要的部门之一。学校，学院和教练中心等教育中心（EC）的选址可能是一个非常复杂的过程。选择一个地点时，必须考虑各种参数，例如人口，识字率，财产成本等。尽管已证明用于站点选择的确定性方法可以提供最佳解决方案，但它们无法用于大型数据集。最近，元启发法在解决优化问题方面变得非常流行。本文提出了两种集成方法，用于EC站点选择的模糊遗传算法（FGA-ECSS）和用于EC站点选择的模糊二进制粒子群优化（FBPSO-ECSS），以最佳地选择站点。为了评估这两种方法的有效性，对FGA-ECSS和FBPSO-ECSS以及遗传算法和二进制粒子群优化算法进行了比较。从提出的解决方案中获得的结果是有希望的，并表明它们可用于解决此类优化问题。