Comparative Study of Type-1 and Interval Type-2 Fuzzy Systems in the Fuzzy Harmony Search Algorithm Applied to Benchmark Functions

At present the use of fuzzy systems applied to problem solving is very common, since the use of linguistic variables is less complex when solving a problem. This article presents a study of the use of type-1 and interval type-2 fuzzy system applied to the solution of problems of optimization using metaheuristic algorithms. There are many types of algorithms that mimic social, biological, etc. behaviors. In this case the work focuses on the metaheuristic algorithms in specific the fuzzy harmony search algorithm (FHS), the metaheuristic algorithms use a technique to obtain a suitable exploration in a definite space to finish with an exploitation around the best position found, with this it is possible to obtain a good solution of the problem. In particular, it was applied to 11 mathematical reference functions using different numbers of dimensions.

[1]  Oscar Castillo,et al.  A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation , 2016, Algorithms.

[2]  Oscar Castillo,et al.  Interval type-2 fuzzy logic for dynamic parameter adaptation in the Harmony search algorithm , 2016, 2016 IEEE 8th International Conference on Intelligent Systems (IS).

[3]  Alireza Askarzadeh,et al.  A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm , 2016 .

[4]  Michio Sugeno,et al.  Fuzzy systems theory and its applications , 1991 .

[5]  Xin-She Yang,et al.  Optimum Tuning of Mass Dampers by Using a Hybrid Method Using Harmony Search and Flower Pollination Algorithm , 2017, ICHSA.

[6]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[7]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[8]  Sasongko Pramono Hadi,et al.  Optimal capacity and placement of distributed generation using metaheuristic optimization algorithm to reduce power losses in Bantul distribution system, Yogyakarta , 2016, 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE).

[9]  Sarat Chandra Swain,et al.  A Harmony Search-Firefly Algorithm Based Controller for Damping Power Oscillations , 2016, 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT).

[10]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[11]  Jerry Mendel,et al.  Type-2 Fuzzy Sets and Systems: An Overview [corrected reprint] , 2007, IEEE Computational Intelligence Magazine.

[12]  Leandro dos Santos Coelho,et al.  A new metaheuristic optimisation algorithm motivated by elephant herding behaviour , 2017 .

[13]  L. Zadeh,et al.  Fuzzy sets and applications : selected papers , 1987 .

[14]  Ruben Romero,et al.  Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation , 2017 .

[15]  Zong Woo Geem,et al.  Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones , 2016 .

[16]  Kusum Deep,et al.  Applications of Harmony Search Algorithm in Data Mining: A Survey , 2015, SocProS.

[17]  Jacques A. Ferland,et al.  A computational study of hybrid approaches of metaheuristic algorithms for the cell formation problem , 2016, J. Oper. Res. Soc..

[18]  Víctor Yepes,et al.  Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms , 2017 .

[19]  Jerry M. Mendel,et al.  Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.