量子免疫算法的改进及其在组合优化中的应用 第4页
The performance of the algorithm can be evaluated by using the optimal value and the convergence condition searched by algorithm. From Table 1, Figure 3 and Figure 4, superiority and inferiority of each algorithm performance can be drawn. Solving the 0-1 knapsack problems, the speed of Greedy algorithm is quicker, but significantly its global optimization ability is worse than quantum genetic algorithm and quantum immune algorithm. At the same time, from the table, the speed of global optimization of the quantum immune algorithm is obviously accelerated. However, due to adding of the operation in vaccination and immune selection, the average running time of the algorithm may be slightly longer than that of Quantum Genetic Algorithm.
6 Conclusion
The quantum immune algorithm, which is proposed by introducing the immune operators and vaccination into the quantum genetic algorithm, can effectively use priori knowledge of the problem and partial optimal solution to speed up the convergence of algorithm to the global optimum solution. It shows more outstanding performance than quantum genetic algorithm for solving 0-1 knapsack problems.
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Li Zhao-hua(1983-), male, Master，major in modern communication technology and intelligent signal processing.
Li Fei(1966-),female,Master Instructor, major in modern communication technology and intelligent signal processing, quantum information processing
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量子免疫算法的改进及其在组合优化中的应用 第4页下载如图片无法显示或论文不完整，请联系qq752018766
