Enhanced Learning in Fuzzy Simulation Models Using Memetic Particle Swarm Optimization

Fuzzy cognitive maps constitute an important simulation methodology that combines neural networks and fuzzy logic. The Fuzzy cognitive maps designed by the experts can be enhanced significantly through learning algorithms, which proved to increase their efficiency and accuracy of simulation. Recently, learning algorithms that employ particle swarm optimization for the minimization of properly defined objective functions have been introduced. In this work, we enhance these learning schemes by incorporating local search in PSO, resulting in a memetic particle swarm optimization learning algorithm. Three variants of the memetic algorithm are applied successfully for the optimization of an Ecological Industrial Park simulation system and they are compared also with the established particle swarm optimization learning schemes. Results are reported and discussed, deriving useful conclusions

I. INTRODUCTION

Simulation has proved to be very useful in understanding and studying both natural and artificial systems. The increasing power of modern computers rendered modeling of high-complexity systems viable, providing better insight into their functioning. Also, simulation has been successfully used to show the eventual real effects of alternative conditions and courses of action, contributing towards the performance optimization of a system [1]. Key issues in simulation are the acquisition of information about the simulated system, the determination of its main features and their interconnections, as well as the accuracy of the simulation outcomes.