Abstract
Quantum genetic algorithm (QGA) is a new optimization algorithm developed recent years, but the standard QGA is easy to get into a local optimal solution. To solve the problem, an improved quantum genetic algorithm (IQGA) is proposed in this paper. IQGA codes the chromosome with probability amplitudes represented by sine and cosine functions, and uses an adaptive strategy of the rotation angle to update the population. Simulation results based on 0-1 knapsack problem demonstrate the effectiveness of IQGA, especially the superiority in terms of optimization quality and population diversity. Moreover, results show the improved algorithm has better comprehensive performance than traditional genetic algorithm (GA) and standard quantum genetic algorithm (QGA).
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call