计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2014年
7期
2566-2571
,共6页
智能优化算法%量子遗传%粒子群算法%最优化%混合算法
智能優化算法%量子遺傳%粒子群算法%最優化%混閤算法
지능우화산법%양자유전%입자군산법%최우화%혼합산법
intelligent optimization algorithms%quantum genetic%PSO%optimization%hybrid algorithm
为提高智能优化算法的性能，将其更好地应用到各个领域，提出了一种两阶段优化算法。在改进的量子遗传算法的基础上，进一步结合粒子群优化算法，构造了量子遗传-粒子群混合算法。通过量子遗传算法对问题进行初步求解，将第一阶段的优化结果作为粒子群算法的初始值，进行第二阶段的问题求解过程，得到问题的最终优化解。通过实验将该算法与传统优化算法进行比较，实验结果表明，该算法在性能方面有一定程度的提高。
為提高智能優化算法的性能，將其更好地應用到各箇領域，提齣瞭一種兩階段優化算法。在改進的量子遺傳算法的基礎上，進一步結閤粒子群優化算法，構造瞭量子遺傳-粒子群混閤算法。通過量子遺傳算法對問題進行初步求解，將第一階段的優化結果作為粒子群算法的初始值，進行第二階段的問題求解過程，得到問題的最終優化解。通過實驗將該算法與傳統優化算法進行比較，實驗結果錶明，該算法在性能方麵有一定程度的提高。
위제고지능우화산법적성능，장기경호지응용도각개영역，제출료일충량계단우화산법。재개진적양자유전산법적기출상，진일보결합입자군우화산법，구조료양자유전-입자군혼합산법。통과양자유전산법대문제진행초보구해，장제일계단적우화결과작위입자군산법적초시치，진행제이계단적문제구해과정，득도문제적최종우화해。통과실험장해산법여전통우화산법진행비교，실험결과표명，해산법재성능방면유일정정도적제고。
To improve the performance of the intelligent optimization algorithm,making the optimization algorithm better appli-cable to various fields,a kind of two phase optimization algorithm was put forward.On the basis of the improved quantum gene-tic algorithm and further combined with particle swarm optimization algorithm,the quantum genetic-mixed particle swarm algo-rithm was constructed.First the problem was solved by quantum genetic algorithm preliminarily,and then the first stage optimi-zation results were taken as initial values for the second stage of the problem solving process to get the final optimization problem solution.The new hybrid algorithm was compared with the traditional optimization algorithm,the experimental results showed that the performance of the new algorithm had certain degree of improvement.
