A local search operator in Quantum Evolutionary Algorithm and its application in Fractal Image Compression

Fractal Image Compression is an optimization problem in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm proposed for class of combinatorial problems like Knapsack problem. While QEA is highly suitable for NP-Hard problems, QEA is not widely used in Fra...

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Bibliographic Details
Main Authors: Mohamadi, Hossein, Nodehi, Ali, Tayarani, Mohamad
Format: Conference Proceeding
Language:English
Subjects:
Convergence
Evolutionary computation
Fractal Image Compression
Fractals
Genetic algorithms
Image coding
NP-hard problem
Partitioning algorithms
Quantum computing
Quantum Evolutionary Algorithm
Simulated annealing
Testing
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Summary:Fractal Image Compression is an optimization problem in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm proposed for class of combinatorial problems like Knapsack problem. While QEA is highly suitable for NP-Hard problems, QEA is not widely used in Fractal Image Compression. In order to improve the performance of QEA in Fractal Image Compression, this paper proposes a local search operator for QEA. The proposed algorithm uses Simulated Annealing algorithm in its search process. The SA is performed on observed possible solution to help the algorithm escaping from local optima. The proposed Simulated Annealing Quantum Evolutionary Algorithm (SAQEA) for fractal image compression is tested on several images like Lena, Pepper and Baboon for several times and is compared with QEA and GA. Experimental results show better performance for the proposed algorithm than QEA and GA and in comparison with full search, the proposed algorithm reaches suitable solutions with much less computation complexity.
DOI:10.1109/ICCAE.2010.5451742