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A DISTRIBUTED QUANTUM EVOLUTIONARY ALGORITHM WITH A NEW CYCLING OPERATOR AND ITS APPLICATION IN FRACTAL IMAGE COMPRESSION

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Author(s): Ali Nodehi | Hosein Mohamadi | Mohamad Tayarani

Journal: International Journal of Artificial Intelligence & Applications
ISSN 0976-2191

Volume: 3;
Issue: 1;
Start page: 163;
Date: 2012;
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Keywords: QUANTUM EVOLUTIONARY ALGORITHM | OPTIMIZATION ALGORITHM | FRACTAL IMAGE COMPRESSION | GENETIC ALGORITHM

ABSTRACT
QUANTUM EVOLUTIONARY ALGORITHM (QEA) IS A NOVEL OPTIMIZATION ALGORITHM, PROPOSED FORCOMBINATORIAL PROBLEMS LIKE KNAPSACK AND TRAP PROBLEMS. WHILE FRACTAL IMAGE COMPRESSION IS INTHE CLASS OF NP-HARD PROBLEMS AND QEA IS HIGHLY SUITABLE FOR THE CLASS OF COMBINATORIALPROBLEMS, QEA IS NOT WIDELY USED IN FRACTAL IMAGE COMPRESSION YET. IN ORDER TO IMPROVE THEPERFORMANCE OF FRACTAL IMAGE COMPRESSION ALGORITHMS, THIS PAPER PROPOSES A DISTRIBUTED QEAWITH A NOVEL OPERATOR CALLED CYCLING QUANTUM EVOLUTIONARY ALGORITHM. IN STANDARD QEA THEDIVERSITY IN THE POPULATION DECREASES ACROSS THE GENERATIONS. DECREASING THE DIVERSITY OF THEPOPULATION DECREASES THE EXPLORATION PERFORMANCE OF THE ALGORITHM AND CAUSES THE ALGORITHMTRAPPING IN THE LOCAL OPTIMA. IN THE PROPOSED ALGORITHM, THERE ARE SOME SUBPOPULATIONS SEARCHINGTHE SEARCH SPACE. AFTER THE SUBPOPULATIONS ARE TRAPPED IN A LOCAL OPTIMUM, THE BEST OBSERVEDPOSSIBLE SOLUTIONS IN THE SUBPOPULATIONS ARE EXCHANGED IN A CYCLIC MANNER. THE PROPOSEDALGORITHM IS USED IN FRACTAL IMAGE COMPRESSION AND EXPERIMENTAL RESULTS ON SEVERAL IMAGES SHOWBETTER PERFORMANCE FOR THE PROPOSED ALGORITHM THAN GENETIC ALGORITHMS AND QEA. IN COMPARISONWITH CONVENTIONAL FRACTAL IMAGE COMPRESSION, THE PROPOSED ALGORITHM FINDS A SUITABLE SOLUTIONWITH MUCH LESS COMPUTATIONAL COMPLEXITY
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