计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2010年
3期
46-48
,共3页
粒子群优化算法%差分进化算法%杂凑算法%测试实验
粒子群優化算法%差分進化算法%雜湊算法%測試實驗
입자군우화산법%차분진화산법%잡주산법%측시실험
particle swarm optimization%differential evolution%hybrid algorithm%testing experiment
结合粒子群优化算法和差分进化算法思想提出了一个杂凑的全局优化算法--PSO-DE,通过对4个基准测试函数的实验测试,并与PSO和DE算法比较,证明新算法在低维(≤10维)搜索空间可以获得更高质量的解.
結閤粒子群優化算法和差分進化算法思想提齣瞭一箇雜湊的全跼優化算法--PSO-DE,通過對4箇基準測試函數的實驗測試,併與PSO和DE算法比較,證明新算法在低維(≤10維)搜索空間可以穫得更高質量的解.
결합입자군우화산법화차분진화산법사상제출료일개잡주적전국우화산법--PSO-DE,통과대4개기준측시함수적실험측시,병여PSO화DE산법비교,증명신산법재저유(≤10유)수색공간가이획득경고질량적해.
A hybrid global optimization algorithm,PSO-DE is presented,which is based on PSO and DE.In order to test PSO-DE,four benchmark functions are used,and the performance of the proposed PSO-DE algorithm is compared with PSO and DE, which demonstrate that it is a more effective global optimization algorithm with high solution quality in the space equal and less than 10 dimensions.
