Hierarchical genetic algorithms for topology optimization in fuzzy control systems

Abstract

We describe in this paper the use of hierarchical genetic algorithms (HGA) for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The HGA enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with two cases of intelligent control. Simulation results for both applications show that we are able to find an optimal set of rules and membership functions for the fuzzy control system.

Fuzzy control of parameters to dynamically adapt the PSO and GA Algorithms

“…The fuzzy system is of Mamdani type because it is more common in this type of fuzzy control and the defuzzification method is the centroid. In this case, we are using this type of defuzzification because in other papers we have achieved good results [14]. Also, the membership functions in the fuzzy main system were chosen of triangular form based on past experiences in this type of fuzzy control.…”

Comparative Study of Interval Type-2 and Type-1 Fuzzy Genetic and Flower Pollination Algorithms in Optimization of Fuzzy Fractional OrderPIλDμControllers

“…The GA is very popular in solving complex engineering problems, because of the feasibility and robustness of GA concepts. However, against the prominent advantages of a GA for determining difficult, constrained, and multi-objective functions where other approaches may have failed, the full strength of the GA in engineering application is yet to be exploited [3,4]. The GA has inadequacy to control parameters which are dynamic in nature.…”

Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems

“…The force acting on mass i from mass j at a specific time t is denoted with F d ij (t) (see (3)), where G(t) is the gravitational constant, which depends on time, M aj is the active gravitational mass related to agent j, M pi is the passive gravitational mass related to agent i, is a small constant and R ij (t) is the Euclidean distance between two agents i and j as defined in (4).…”

“…According to G(t), the total force in different directions is calculated. In particular, the algorithm computes the Euclidean distance between two agents R ij (t) and the force acting on mass i from mass j at a specific time t (see (3)). Subsequently, the total force F d i (t) (see (6)) is computed.…”

“…The issue of tuning some parameters of a search algorithm is one of the most important areas of research in evolutionary computation [1]. This research line was followed by Montiel et al [2] and Castillo et al [3], which treated the idea of adjusting an evolutionary algorithm.…”