A Fuzzy Bee Colony Optimization Algorithm Using an Interval Type-2 Fuzzy Logic System for Trajectory Control of a Mobile Robot

• Published in: Advances in Artificial Intelligence and Soft Computing Vol. 9413; pp. 460 - 471
• Main Authors: Amador-Angulo, Leticia, Castillo, Oscar
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Bee; Colony optimization algorithm; Computer vision; Dynamic adaptation; Health & safety aspects of computing; Interval Type-2 Fuzzy Logic System; Mobile robots
• ISBN: 9783319270593; 3319270591
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-27060-9_38

A new fuzzy Bee Colony Optimization (FBCO) algorithm with dynamic adaptation in the alpha and beta parameters using an Interval Type-2 Fuzzy Logic System is presented in this paper. The Bee Colony Optimization meta-heuristic belongs to the class of Nature-Inspired Algorithms. The objective of the work is based on the use of Interval Type-2 Fuzzy Logic to find the best Beta and Alpha parameter values in BCO. We use BCO specifically for tuning membership functions of the fuzzy controller for stability of the trajectories in a mobile robot. We implemented the IAE and MSE metrics as performance metrics of control. We added perturbations in the model with the pulse generator so that the Interval Type-2 Fuzzy Logic System is better analyzed under uncertainty and to verify that the FBCO shows better results than the traditional BCO.