Design of Fuzzy Controller for Car Parking Problem Using Evolutionary Multi-objective Optimization Approach

This paper proposes an automatic method to design a fuzzy logic controller for the automated car parking problem within the multi-objective evolutionary optimization framework, which requires three factors: an encoding scheme, design of evaluation criteria, and design of evolutionary operations. Parameters of antecedent membership functions and consequent parameter vectors are encoded into a chromosome so that antecedent/consequent parameters and rule structure of a fuzzy logic controller are optimized simultaneously during evolution. Three optimization criteria are proposed to find a set of good fuzzy logic controllers using Pareto optimality. The proposed algorithm is applied to the well-known car parking problem and produced a Pareto set of fuzzy logic controllers that can control the motion of a vehicle for automated parking. Each fuzzy logic controller in the set has unique characteristics and can be selected according to users' preferences, which is one of the major advantages of using the multi-objective evolutionary optimization