Fuzzy Logic Based Optimization Algorithm

Abstract

Through the history, humans have been succeeded by solving multiple problems during their day to day life. They use simple rules of thumb from their past experiences to solve several difficulties. Under such circumstances, many researchers have tried to emulate the human reasoning based on mathematical approaches. Based on simple if-then rules, fuzzy logic is one of the disciplines in artificial intelligence which emulates the human reasoning in terms of linguistic variables. In fuzzy logic, linguistic variables represent natural language variables which humans commonly used to specify semantic rules from several processes. On the other hand, metaheuristics have been proposed as alternative search mechanisms to find optimal solutions for complex optimization problems where classical mathematical methodologies present some limitations by working under multimodal surfaces. This chapter presents a novel metaheuristic algorithms called Fuzzy Logic Optimization Algorithm (FLOA). The proposed algorithm models the search strategy which an expert human in optimization could follow to solve optimization problems based on simple if-then rules. The FLOA, uses a Takagi-Sugeno inference model, where the output is a weighted sum of four fuzzy rules; Attraction, repulsion, perturbation and randomness. The performance of the proposed method is compared against the performance results of several state-of-art metaheuristics, evaluating several test functions. The numerical results are statistical validated using a non-parametric framework to eliminate the random effect.