Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot

Abstract

A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm.

Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

“…The BCO algorithm has proven to be a good technique to optimize parameters [35,63], which is why we perform an automated search that allows better performance of the BCO algorithm.…”

“…At present the works related to the dynamic adjustment of parameters in BCO are: in [35], a Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot is shown; in [36], an improved artificial bee colony algorithm for solving constrained optimization problems is presented; in [37], a survey of swarm intelligence for dynamic optimization: algorithms and applications is shown; and, in [21], an Efficient cooperative relaying in flying ad hoc networks using fuzzy-bee colony optimization is presented, among others.…”

A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot

“…The controller is of Mamdani type, so that the input and output parameters are represented by linguistic variables. The input variables are the error in the linear velocity (ev) and angular velocity (ew), and the output variables are the right (T1) and left (T2) torques [9,10], which are represented in Figure 11. If (e v is N) and (e w is N) then (T1 is N) (T2 is N).…”

On Characterizations of Directional Derivatives and Subdifferentials of Fuzzy Functions

“…They [22] introduced a fuzzy harmony search algorithm with fuzzy logic and this algorithm was utilized to solve the fuzzy optimization problem. Amador et al [23], presented a new optimization algorithm based on the fuzzy logic system.…”

“…In order to solve complex optimization problems in real life, various optimization algorithms have been presented in [9,10,[15][16][17][18][19][20][21][22][23]. Jia et al [9] presented a new algorithm for solving the optimization problem based on the stock exchange.…”