一种解决组合优化问题的量子遗传算法QGA

A Quantum Genetic Algorithm to Solve Combinatorial Optimization Problem

本文在量子变异的基础上 ,提出了一种解决组合优化问题的量子遗传算法QGA ,它融合了遗传量子算法GQA和经典遗传算法的优点 ,只用一个个体就可在很短的时间内搜索到最优解 ,并针对一个典型的组合优化问题——— 0 / 1背包问题进行了对比实验 ,实验结果表明本文所提出的量子遗传算法QGA优于传统遗传算法和遗传量子算法GQA .

Based on quantum mutation, a quantum genetic algorithm (QGA) to solve combinatorial optimization problem is proposed.It has good features of genetic quantum algorithm (GQA) and traditional genetic algorithm,which can obtain the best solution with one chromosome in a short time.Comparing experiments have been conducted on a typical combinatorial optimization problem——0/1 knapsack problem.Experimental results have shown that the proposed quantum genetic algorithm is superior to genetic quantum algorithm and traditional genetic algorithm on performance.