FLC control for tuning exploration phase in bio-inspired metaheuristic

Kazimierz Kiełkowicz, Damian Grela

Abstract


Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.


Keywords


Bat algorithm, swarm intelligence, metaheuristics, optimization, fuzzy logic, Mamdami-Type inference system

Full Text:

PDF

References


R. C. Eberhart, Y, Shi, “Empirical Study of Particle Swarm Optimization”, 1999.

D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Kluwer Academic Publishers, 1989.

R. Storn, K. Price, “Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces”, Technical Report, 1995.

J. Kennedy, R. C. Eberhart, “Particle swarm optimization”, In Proc. of IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948.

M. Dorigo, V. Maziezzo, A. Colorni, “The ant system: optimization by a colony of cooperating ants”, IEEE Trans. on Systems, Man and Cybernetics B, vol. 26, no. 1, 1996, pp. 29–41.

X. S. Yang, “A New Metaheuristic Bat-Inspired Algorithm”, Nature Inspired Cooperative Strategies for Optimization, 2010, pp. 65-74.

X. S. Yang, “Bat Algorithm for Multi-Objective Optimization”, International Journal of Bio-Inspired Computation, vol. 3, issue 5, 2011, pp. 267-274

S. Fong, X. S. Yang, M. Karamanglu, “Bat Algorithm for Topology Optimization in Microelectronic Application”, International Conference on Future Generation Communication Technology (FGCT), IEEE, 2012, pp. 150-155.

I. Fister Jr, D. Fister, X. S. Yang, “A Hybrid Bat Algorithm”, Elektrotehniski Vestnik, 2013, pp. 1-7.

A. Baziar, A. A. Kavoosi-Fard, J. Zare, “A Novel Self Adoptive Modification Approach Based on Bat Algorithm for Optimal Management of Renewable MG”, Journal of Intelligent Learning System and Application, vol. 5, issue 1, 2013, pp. 11-18

S. Mirjalili, S. M. Mirjalili, Xin-She Yang, “Binary Bat Algorithm”, Neural Computing and Applications, vol. 25, issue 3, 2014, pp. 663-681.

K. Kiełkowicz, D. Grela, “Modified Bat Algorithm for Nonlinear Optimization”, International Journal of Computer Science and Network Security (IJCSNS), 2016, pp. 46-50.

G. Wang and L. Guo, “A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization”, Journal of Applied Mathematics, vol. 2013, 2013, pp. 1-21.

S. Yilmaz and E. U. Kucuksille, “Improved Bat Algorithm (IBA) on Continuous Optimization Problems”, Lecture Notes on Software Engineering, Vol. 1, No. 3, 2013, pp. 279-283.

X. Wang, W. Wang, Y. Wang, “An Adaptive Bat Algorithm”, Lecture Notes in Computer Science vol. 7996, 2013, pp. 216-223.

I. Fister Jr., S. Fong, J. Brest, and I. Fister, “A Novel Hybrid Self-Adaptive Bat Algorithm”, The Scientific World Journal, 2014, pp. 1-12.

P. Melin, F. Olivas, O. Castillo, F. Valdez, J. Soria, M. Valdez, “Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic”, Expert Systems with Applications, vol. 40, issue 8, 2013, pp. 3196–3206.

Juan Rada-Vilela. FuzzyLite: A Fuzzy Logic Control Library, 2017. URL http://www.fuzzylite.com.

W. Gao, S. Liu, “A Modified Artificial Bee Colony Algorithm”, Computers and Operations Research, vol. 39, 2012, pp. 687–697.




DOI: http://dx.doi.org/10.17951/ai.2016.16.2.32
Date of publication: 2017-12-22 09:38:07
Date of submission: 2017-12-22 09:25:34


Statistics


Total abstract view - 1331
Downloads (from 2020-06-17) - PDF - 0

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 Kazimierz Kiełkowicz, Damian Grela

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.