Fuzzy Dynamic Adaptation of Gap Generation and Mutation in Genetic Optimization of Type 2 Fuzzy Controllers

Abstract

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.

Recommendations on designing practical interval type-2 fuzzy systems

“…For example, the popular ANFIS model for T1 fuzzy systems [24] combines mathematical programming (least squares estimation) and a gradient-based algorithm in its optimization. EC algorithms are recommended for the optimization of IT2 fuzzy systems, because derivatives are difficult to compute in an IT2 fuzzy system (especially when the LMF and/or UMF formulas include tests about the location of their independent variable), and such algorithms are globally convergent [34], [4], [15], [44], [28]. There are many such EC algorithms, e.g., genetic algorithms, simulated annealing, particle swarm optimization, etc.…”

“…3 Again, we did not count the number of publications about type II and interval-valued fuzzy sets and systems here. 4 https://cis.ieee.org/getting-involved/awards/past-recipients#TFSOutstandingPaperAward on interval-valued fuzzy sets [26], which are closely related to IT2 fuzzy sets [2], [38], [45]. Remarkably, five of them were awarded very recently.…”

Incremental SMC-based CNF control strategy considering magnetic ball suspension and inverted pendulum systems through cuckoo search-genetic optimization algorithm

“…time consuming to set the parameters to reach the optimum condition. Therefore, by defining the cost function in the form of the following equation and using the GC algorithm, the suitable coefficients with the least error rate are introduced [24][25][26]:…”

Decision making viva genetic algorithm for the utilization of leftovers

“…The minimization of leftovers discussed so far needs proper decision making as well as proper simulation environment to make them usable materials for different construction purposes. For instance, a decision-based approach is used on the basis of fuzzy logic for the use of gap generation mutation probability in a GA. 40 The test on benchmark problems showed the minimum errors during flight of an airplane happened due to the combination of fuzzy logic with some of the parameter of GA. Furthermore, a fuzzy decision-making approach is used for fuzzy adaptation parameters in harmony search.…”