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International Conference on Informatics and Systems

Quantum crossover based quantum genetic algorithm for solving non-linear programming

Mohammed, Elhefhawy, El-Sherbiny, Hadhoud

Published Jan 1, 2012• 0 citations
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Based on quantum genetic algorithm(QGA),a novel improved quantum genetic algorithm(NIQGA)to solve combinatorial optimization problem is proposed.To make full use of interference and entanglement characteristics of quantum state,dynamic step length in adjustment of angle of quantum gate,quantum crossover operation and quantum mutation operation are introduced,therefore high efficiency for optimization is achieved.Two typical combinatorial optimization problems—0/1 knapsack problem and route selection problem,are adopted to confirm the performance of NIQGA.Experimental results show that compared with GA and QGA,NIQGA is characterized by fast convergence rate and excellent capability on global optimization,especially better performance for combinatorial optimization problem with less correlation of genes.
In this paper, we propose an improved quantum interference crossover-based genetic algorithm. The primary difference between the classical interference crossover and the new one is the chromosome reconstruction process. Unlike the position-based selection approach in classical approach, the novel method selects the city that has a shorter distance with the city selected in the previous chromosome. The new method has been used to solve the traveling salesman problem. Experimental results indicate that the new method is superior to the classical interference crossover-based genetic algorithm.