Optimizing the Membership Degree of Fuzzy Inference System (FIS) and Fuzzy Clustering Means (FCM) in Weather Data Using Firefly Algorithm

Abstract

In weather clustering, there are many variables which can be observed such as air temperature, humidity, sunlight intensity, and so on. In this research, Takagi-Sugeno Fuzzy Inference System (FIS) will be used for forecasting the sunlight intensity based on temperature and humidity and Fuzzy Clustering Means (FCM) will be used for clustering them based on fuzzy set. From the data consisting of temperature, humidity, and sunlight intensity, we will forecast sunlight intensity and cluster them into two clusters, three clusters, and four clusters by FCM method. In FIS method, the membership degree are often generated by trial and error. Also, the optimization of the initial of membership degree are required in FCM. Because the initial of membership degree are often generated by trial and error, in this research, we use heuristic method like Firefly Algorithm to optimize the membership degree. From the simulations, Firefly Algorithm can optimize the membership degree of FIS for forecasting the data with minimum Mean Square Error (MSE) and the initial of membership degree of FCM with two clusters, three clusters, and four clusters with minimum objective value.