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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.

This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.

Abstract:In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithms (original and modified versions) are compared with the design of fuzzy systems used for controlling the trajectory of an autonomous mobile robot. Simulation results reveal that PSO algorithm outperforms the results of the BCO and BA algorithms.

In this paper a comparison of the use of Type-1 and Type-2 fuzzy logic in the benchmark problem known as the problem of the water tank is presented. Fuzzy logic allows managing uncertainty, which is very common in linguistic fuzzy systems. It has been shown that Type-2 fuzzy systems manage better the uncertainty in real world problems but, evidence exists also to show that using Type-2 technology is usually computationally more expensive. It is shown in this paper using an experimental basis that an interval Type-2 Fuzzy System presents better results than those obtained with the traditional way of handling fuzzy systems with Type-1 fuzzy logic. Experiments and comparisons are presented to validate the proposed approach.

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A new approach for the Bee Colony Optimization algorithm (BCO) for Type-1 and Type-2 Fuzzy Controller design is presented in this paper. The Bee Colony Optimization meta-heuristic belongs to the class of Nature-Inspired Algorithms. The main objective of the work is based on the main reasons for the optimization of Type-1 and Type-2 Fuzzy Controllers, specifically in tuning membership functions of the fuzzy controller for the benchmark problem known as the water tank. For the design of Type-1 and Type-2 Fuzzy Controllers in particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate values of the parameters and the structure of the fuzzy systems.