Evolutionary computation biased fuzzy membership functions optimization

This paper presents a comparative optimization performance study among three evolutionary computational techniques as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Hybrid Particle Swarm with Mutation (HPSOM) methods by automatically adjusting the fuzzy membership functions. For comparative study performances, the above-mentioned techniques are firstly used to generate an optimal set of parameters for fuzzy reasoning model based on either their initial subjective selection, or on a random selection. The implementation process is presented and tested with promising results. The case study used is an application designed to park a vehicle into a garage, beginning from any start position. Finally the obtained results are discussed.