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

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.

[1]  Harish Garg,et al.  An efficient biogeography based optimization algorithm for solving reliability optimization problems , 2015, Swarm Evol. Comput..

[2]  Oscar Castillo,et al.  Modification of the Bat Algorithm using fuzzy logic for dynamical parameter adaptation , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[3]  Zhongping Wan,et al.  An improved artificial bee colony algorithm for solving constrained optimization problems , 2015, International Journal of Machine Learning and Cybernetics.

[4]  Oscar Castillo,et al.  Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation , 2016, Appl. Soft Comput..

[5]  Dušan Teodorović,et al.  Pre-timed control for an under-saturated and over-saturated isolated intersection: a Bee Colony Optimization approach , 2017 .

[6]  Xin-She Yang,et al.  New directional bat algorithm for continuous optimization problems , 2017, Expert Syst. Appl..

[7]  Mounir Ayadi,et al.  PID-type fuzzy logic controller tuning based on particle swarm optimization , 2012, Eng. Appl. Artif. Intell..

[8]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[9]  Harish Garg,et al.  An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm , 2015 .

[10]  Stefan Preitl,et al.  Gravitational Search Algorithms in Fuzzy Control Systems Tuning , 2011 .

[11]  Harish Garg,et al.  Reliability, Availability and Maintainability Analysis of Industrial Systems Using PSO and Fuzzy Methodology , 2014 .

[12]  Grigorios N. Beligiannis,et al.  Optimizing Shift Scheduling for Tank Trucks Using an Effective Stochastic Variable Neighbourhood Approach , 2016 .

[13]  Changhe Li,et al.  A survey of swarm intelligence for dynamic optimization: Algorithms and applications , 2017, Swarm Evol. Comput..

[14]  Harish Garg,et al.  An efficient two phase approach for solving reliability-redundancy allocation problem using artificial bee colony technique , 2013, Comput. Oper. Res..

[15]  Olympia Roeva,et al.  Bat algorithm with parameter adaptation using Interval Type-2 fuzzy logic for benchmark mathematical functions , 2016, 2016 IEEE 8th International Conference on Intelligent Systems (IS).

[16]  Rabindra Kumar Sahu,et al.  A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems , 2015 .

[17]  Harish Garg,et al.  Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment , 2014, Expert Syst. Appl..

[18]  Dusan Ramljak,et al.  Bee colony optimization for the p-center problem , 2011, Comput. Oper. Res..

[19]  Milan Tuba,et al.  Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[20]  Dušan Teodorović,et al.  Area-wide urban traffic control: A Bee Colony Optimization approach , 2017 .

[21]  T. Chatchanayuenyong,et al.  Design and Development of an Intelligent Control by Using Bee Colony Optimization Technique , 2012 .

[22]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[23]  Harish Garg,et al.  A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data , 2015 .

[24]  Goran Z. Markovic,et al.  Routing and wavelength assignment in all-optical networks based on the bee colony optimization , 2007, AI Commun..

[25]  J. Amudhavel,et al.  Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem , 2017, Comput. Intell. Neurosci..

[26]  Harish Garg,et al.  Performance analysis of an industrial system using soft computing based hybridized technique , 2017 .

[27]  Oscar Castillo,et al.  Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm , 2017, Soft Comput..

[28]  Fevrier Valdez,et al.  Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot , 2012, Inf. Sci..

[29]  Leticia Amador-Angulo,et al.  A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers , 2016, Soft Computing.

[30]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[31]  Mousumi Basu,et al.  Bee colony optimization for combined heat and power economic dispatch , 2011, Expert Syst. Appl..

[32]  Mauro Dell’Orco,et al.  Solving the gate assignment problem through the Fuzzy Bee Colony Optimization , 2017 .

[33]  R. K. Sarin,et al.  Identification of Type-2 Fuzzy Models for Time-Series Forecasting Using Particle Swarm Optimization , 2012, 2012 International Conference on Communication Systems and Network Technologies.

[34]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[35]  Oscar Castillo,et al.  New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system , 2015, Inf. Sci..

[36]  U. Sabura Banu.  Implementation of Fractional Order PID Controller for Three Interacting Tank Process Optimally Tuned Using Bee Colony Optimization , 2013, SEMCCO.

[37]  Harish Garg,et al.  Multi-objective reliability-redundancy allocation problem using particle swarm optimization , 2013, Comput. Ind. Eng..

[38]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[39]  Taher Niknam,et al.  A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem , 2010 .

[40]  Juing-Shian Chiou,et al.  PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design , 2016, Micromachines.

[41]  Oscar Castillo,et al.  Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic , 2016, Soft Comput..

[42]  Z. Johanyák,et al.  A Hybrid Algorithm for Parameter Tuning in Fuzzy Model Identification , 2012, Acta Polytechnica Hungarica.

[43]  Li-Pei Wong,et al.  A Bee Colony Optimization Algorithm for Traveling Salesman Problem , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).

[44]  Gautam Sarkar,et al.  Discrete Wavelet Transform based V-I image fusion with Artificial Bee Colony Optimization , 2017, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).

[45]  Oscar Castillo,et al.  Modification of the Bat Algorithm Using Type-2 Fuzzy Logic for Dynamical Parameter Adaptation , 2017, Nature-Inspired Design of Hybrid Intelligent Systems.

[46]  Claudia-Adina Dragos,et al.  Iterative performance improvement of fuzzy control systems for three tank systems , 2012, Expert Syst. Appl..

[47]  Kevin D. Seppi,et al.  Adaptive diversity in PSO , 2006, GECCO '06.

[48]  Oscar Castillo,et al.  Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic , 2013, Expert Syst. Appl..

[49]  Harish Garg,et al.  A hybrid PSO-GA algorithm for constrained optimization problems , 2016, Appl. Math. Comput..

[50]  Oscar Castillo,et al.  Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot , 2016, Sensors.

[51]  Roozbeh Torkzadeh,et al.  Design of GA optimized fuzzy logic-based PID controller for the two area non-reheat thermal power system , 2013, 2013 13th Iranian Conference on Fuzzy Systems (IFSC).

[52]  Oscar Castillo,et al.  A review on the design and optimization of interval type-2 fuzzy controllers , 2012, Appl. Soft Comput..

[53]  Nguyen Quoc Thanh,et al.  Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization , 2017, Landslides.

[54]  Patricia Melin,et al.  Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..

[55]  Krzysztof Wiktorowicz,et al.  Evaluation of selected fuzzy particle swarm optimization algorithms , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[56]  Stefan Preitl,et al.  Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers , 2014, Expert Syst. Appl..

[57]  Ching-Hung Lee,et al.  Tracking control of unicycle-modeled mobile robots using a saturation feedback controller , 2001, IEEE Trans. Control. Syst. Technol..

[58]  Milos Nikolic,et al.  Empirical study of the Bee Colony Optimization (BCO) algorithm , 2013, Expert Syst. Appl..

[59]  Harish Garg.  Solving structural engineering design optimization problems using an artificial bee colony algorithm , 2013 .

[60]  Kathiravan Srinivasan,et al.  Efficient cooperative relaying in flying ad hoc networks using fuzzy-bee colony optimization , 2017, The Journal of Supercomputing.

[61]  Danielle Azar,et al.  A Combined Ant Colony Optimization and Simulated Annealing Algorithm to Assess Stability and Fault-Proneness of Classes Based on Internal Software Quality Attributes , 2016 .

[62]  Harish Garg,et al.  Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process , 2017, Applied Intelligence.

[63]  Oscar Castillo,et al.  A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot , 2015, Appl. Soft Comput..

[64]  Issarachai Ngamroo,et al.  A BEE COLONY OPTIMIZATION BASED-FUZZY LOGIC-PID CONTROL DESIGN OF ELECTROLYZER FOR MICROGRID STABILIZATION , 2012 .

[65]  Oscar Castillo,et al.  An interval type-2 fuzzy logic system for dynamic parameter adaptation in particle swarm optimization , 2014, 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW).