Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

Beklaryan, Gayane L.; Akopov, Andranik S.; Khachatryan, Nerses K.

doi:10.2478/cait-2019-0017

Abstract

This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness from a potential extremum. Results of optimisation experiments demonstrate the greater time-efficiency of F-RCGA in comparison with other RCGAs, as well as the Monte-Carlo method. F-RCGA was validated by using well-known test instances and applied for the optimisation of characteristics of some system dynamics models.

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Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

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