Fuzzy Logic in Dynamic Parameter Adaptation of Harmony Search Optimization for Benchmark Functions and Fuzzy Controllers

Nowadays the use of fuzzy logic has been increasing in popularity, and this is mainly due to the inference mechanism that allows simulating human reasoning in knowledge-based systems. The main contribution of this work is using the concepts of fuzzy logic in a method for dynamically adapting the main parameters of the harmony search algorithm during execution. Dynamical adaptation of parameters in metaheuristics has been shown to improve performance and accuracy in a wide range of applications. For this reason, we propose and approach for fuzzy adaptation of parameters in harmony search. Two case studies are considered for testing the proposed approach, the optimization of mathematical functions, which are unimodal, multimodal, hybrid, and composite functions and a control problem without noise and when noise is considered. A statistical comparison between the harmony search algorithm and the fuzzy harmony search algorithm is presented to verify the advantages of the proposed approach.

[1]  Martín Montes Rivera,et al.  Comparative of Effectiveness When Classifying Colors Using RGB Image Representation with PSO with Time Decreasing Inertial Coefficient and GA Algorithms as Classifiers , 2018, Fuzzy Logic Augmentation of Neural and Optimization Algorithms.

[2]  S.M.T Bathaee,et al.  Load Frequency Control in Interconnected Power System by Nonlinear Term and Uncertainty Considerations by Using of Harmony Search Optimization Algorithm and Fuzzy-Neural Network , 2018, Electrical Engineering (ICEE), Iranian Conference on.

[3]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[4]  Oscar Castillo,et al.  A high-speed interval type 2 fuzzy system approach for dynamic parameter adaptation in metaheuristics , 2019, Eng. Appl. Artif. Intell..

[5]  Oscar Castillo,et al.  A new optimization meta-heuristic algorithm based on self-defense mechanism of the plants with three reproduction operators , 2018, Soft Comput..

[6]  Zong Woo Geem,et al.  Harmony Search for Generalized Orienteering Problem: Best Touring in China , 2005, ICNC.

[7]  Oscar Castillo,et al.  A fuzzy hierarchical operator in the grey wolf optimizer algorithm , 2017, Appl. Soft Comput..

[8]  Aman Jantan,et al.  Hybridizing Bat Algorithm with Modified Pitch Adjustment Operator for Numerical Optimization Problems , 2017 .

[9]  Oscar Castillo,et al.  Statistical Comparison of the Bee Colony Optimization and Fuzzy BCO Algorithms for Fuzzy Controller Design Using Trapezoidals MFs , 2016, WCSC.

[10]  Mohammed Azmi Al-Betar,et al.  Economic load dispatch problems with valve-point loading using natural updated harmony search , 2018, Neural Computing and Applications.

[11]  Chih-Min Lin,et al.  Fuzzy cerebellar model articulation controller network optimization via self-adaptive global best harmony search algorithm , 2018, Soft Comput..

[12]  Oscar Castillo,et al.  A new randomness approach based on sine waves to improve performance in metaheuristic algorithms , 2020, Soft Comput..

[13]  Hossam Faris,et al.  Optimizing connection weights in neural networks using the whale optimization algorithm , 2016, Soft Computing.

[14]  Yuhui Shi,et al.  Multimodal optimization using particle swarm optimization algorithms: CEC 2015 competition on single objective multi-niche optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[15]  Raimund Rolfes,et al.  The Performance of a Modified Harmony Search Algorithm in the Structural Identification and Damage Detection of a Scaled Offshore Wind Turbine Laboratory Model , 2018 .

[16]  Oscar Castillo,et al.  Fuzzy granular gravitational clustering algorithm for multivariate data , 2014, Inf. Sci..

[17]  Behnam Mohammadi-Ivatloo,et al.  Improved harmony search algorithm for the solution of non-linear non-convex short-term hydrothermal scheduling , 2018 .

[18]  Meng Sun,et al.  Study on an Adaptive Co-Evolutionary ACO Algorithm for Complex Optimization Problems , 2018, Symmetry.

[19]  Oscar Castillo,et al.  A new fuzzy-fractal-genetic method for automated mathematical modelling and simulation of robotic dynamic systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[20]  Oscar Castillo,et al.  Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller , 2018, Adv. Oper. Res..

[21]  Ying Tan,et al.  Exponentially decreased dimension number strategy based dynamic search fireworks algorithm for solving CEC2015 competition problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[22]  Oscar Castillo,et al.  Improved Method Based on Type-2 Fuzzy Logic for the Adaptive Harmony Search Algorithm , 2018, Fuzzy Logic Augmentation of Neural and Optimization Algorithms.

[23]  K. Arthi,et al.  Zone-based dual sub sink for network lifetime maximization in wireless sensor network , 2018, Cluster Computing.

[24]  Fayez F. Boctor,et al.  An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case , 2018, Eur. J. Oper. Res..

[25]  Mohammad El-Bardini,et al.  Embedded two level direct adaptive fuzzy controller for DC motor speed control , 2015 .

[26]  Eric Alfredo Rincón García,et al.  Adaptation of the musical composition method for solving constrained optimization problems , 2014, Soft Comput..

[27]  Liying Zhao,et al.  Improved Harmony Search Algorithm for Truck Scheduling Problem in Multiple-Door Cross-Docking Systems , 2018 .

[28]  Oscar,et al.  [Studies in Fuzziness and Soft Computing] Type-2 Fuzzy Logic: Theory and Applications Volume 223 || 3 Type-2 Fuzzy Logic , 2008 .

[29]  Hayder Salim Hameed,et al.  Brushless DC motor controller design using MATLAB applications , 2018, 2018 1st International Scientific Conference of Engineering Sciences - 3rd Scientific Conference of Engineering Science (ISCES).

[30]  Patricia Melin,et al.  Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm , 2017, Algorithms.

[31]  Mojtaba Shivaie,et al.  A Stochastic Framework for Multi-stage Generation Expansion Planning under Environmental and Techno-economic Constraints , 2016 .

[32]  Yong Wang,et al.  On the selection of solutions for mutation in differential evolution , 2016, Frontiers of Computer Science.

[33]  Mario Ponce,et al.  A Trajectory Tracking Control for a Boost Converter‑Inverter‑DC Motor Combination , 2018, IEEE Latin America Transactions.

[34]  M. R. Rashmi,et al.  Optimal sizing and distribution system reconfiguration of hybrid FC/WT/PV system using cluster computing based on harmony search algorithm , 2019, Cluster Computing.

[35]  Quan-Ke Pan,et al.  An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem , 2018, Swarm Evol. Comput..

[36]  Jie Luo,et al.  An evolutionary algorithm based on decomposition for multimodal optimization problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[37]  Paul Manuel,et al.  The All-Ones Problem for Binomial Trees, Butterfl‡y and Benes Networks , 2012, SOCO 2012.

[38]  Bo Hu,et al.  Competition harmony search algorithm with dimension selection for continuous optimization probems , 2018, 2018 Chinese Control And Decision Conference (CCDC).

[39]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .