Solving unbounded knapsack problem based on quantum genetic algorithms

Authors:
Rung-Ching Chen;Yun-Hou Huang;Ming-Hsien Lin
Affiliations:
Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung Country, Taiwan, R.O.C;Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung Country, Taiwan, R.O.C;Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung Country, Taiwan, R.O.C
Venue:
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Year:
2010

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Abstract

Resource distribution, capital budgeting, investment decision and transportation question could form as knapsack question models. Knapsack problem is one kind of NP-Complete problem and Unbounded Knapsack problems (UKP) are more complex and harder than general Knapsack problems. In this paper, we apply QGAs (Quantum Genetic Algorithms) to solve Unbounded Knapsack Problem. First, present the problem into the mode of QGAs and figure out the corresponding genes types and their fitness functions. Then, find the perfect combination of limitation and largest benefit. Finally, quant bit is updated by adjusting rotation angle and the best solution is found. In addition, we use the strategy of greedy method to arrange the sequence of chromosomes to enhance the effect of executing. Preliminary experiments prove our system is effective. The system also compare with AGAs (Adaptive Genetic Algorithms) to estimate their performances.