AIRCC's International Journal of Computer Science and Information Technology

Md. Amjad Hossain ¹, Pintu Chandra Shill ², Bishnu Sarker ¹, Kazuyuki Murase ²

Affiliations
1 Department of Computer Science and Engineering, KUET, Khulna 9203, BD
2 Department of System Design Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, JP
3 Department of Computer science and Engineering, KUET, Khulna 9203, BD

Abstract

Fuzzy rule based models have a capability to approximate any continuous function to any degree of accuracy on a compact domain. The majority of FLC design process relies on heuristic knowledge of experience operators. In order to make the design process automatic we present a genetic approach to learn fuzzy rules as well as membership function parameters. Moreover, several statistical information criteria such as the Akaike information criterion (AIC), the Bhansali-Downham information criterion (BDIC), and the Schwarz-Rissanen information criterion (SRIC) are used to construct optimal fuzzy models by reducing fuzzy rules. A genetic scheme is used to design Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule parameters and the identification of the consequent parameters. Computer simulations are presented confirming the performance of the constructed fuzzy logic controller.

Keywords

Genetic Algorithms (GAs), Fuzzy Logic Controller (FLC), Statistical Information Criteria, Singular Value Decomposition (SVD), Takagi-Sugeno-Kang (TSK) Model.

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Md. Amjad Hossain ¹, Md. Kowsar Hossain ¹, M. M. A. Hashem ¹

Affiliations
1 Department of Computer Science and Engineering, Khulna University of Engineering and Technology (KUET), Khulna, 9203, BD

Abstract

This paper proposes a generalized Hybrid Real-coded Quantum Evolutionary Algorithm (HRCQEA) for optimizing complex functions as well as combinatorial optimization. The main idea of HRCQEA is to devise a new technique for mutation and crossover operators. Using the evolutionary equation of PSO a Single-Multiple gene Mutation (SMM) is designed and the concept of Arithmetic Crossover (AC) is used in the new Crossover operator. In HRCQEA, each triploid chromosome represents a particle and the position of the particle is updated using SMM and Quantum Rotation Gate (QRG), which can make the balance between exploration and exploitation. Crossover is employed to expand the search space, Hill Climbing Selection (HCS) and elitism help to accelerate the convergence speed. Simulation results on Knapsack Problem and five benchmark complex functions with high dimension show that HRCQEA performs better in terms of ability to discover the global optimum and convergence speed.

Keywords

Hybrid Algorithm, Evolutionary Algorithm, Particle Swarm Optimization, Quantum Evolutionary Algorithm, Arithmetic Crossover.

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