计算机研究与发展
計算機研究與髮展
계산궤연구여발전
JOURNAL OF COMPUTER RESEARCH AND DEVELOPMENT
2001年
6期
746-753
,共8页
常微分方程组%建模%遗传程序设计%遗传算法%局部搜索%模拟退火
常微分方程組%建模%遺傳程序設計%遺傳算法%跼部搜索%模擬退火
상미분방정조%건모%유전정서설계%유전산법%국부수색%모의퇴화
以人口模型和化学反应模型为例,通过大量实验研究比较了分别采用基于两种传统的搜索方法即局部搜索算法和模拟退火算法、遗传程序设计方法(简称GP)以及遗传算法(简称GA)四者相互结合的14种不同算法建立动态系统的常微分方程组模型的实验结果,得到了有关各算法性能比较的一些新的结论.两个实例的实验结果表明:在14种算法中,GP+GA+LS〖KG-*8〗MU算法(即在采用GP的模型结构的优化过程中嵌入采用GA的模型参数的优化过程,并且在每一演化代对种群中的部分个体进行基于GP的标准变异算子产生邻域解的局部搜索过程)是目前解决常微分方程组建模问题的最好算法.
以人口模型和化學反應模型為例,通過大量實驗研究比較瞭分彆採用基于兩種傳統的搜索方法即跼部搜索算法和模擬退火算法、遺傳程序設計方法(簡稱GP)以及遺傳算法(簡稱GA)四者相互結閤的14種不同算法建立動態繫統的常微分方程組模型的實驗結果,得到瞭有關各算法性能比較的一些新的結論.兩箇實例的實驗結果錶明:在14種算法中,GP+GA+LS〖KG-*8〗MU算法(即在採用GP的模型結構的優化過程中嵌入採用GA的模型參數的優化過程,併且在每一縯化代對種群中的部分箇體進行基于GP的標準變異算子產生鄰域解的跼部搜索過程)是目前解決常微分方程組建模問題的最好算法.
이인구모형화화학반응모형위례,통과대량실험연구비교료분별채용기우량충전통적수색방법즉국부수색산법화모의퇴화산법、유전정서설계방법(간칭GP)이급유전산법(간칭GA)사자상호결합적14충불동산법건립동태계통적상미분방정조모형적실험결과,득도료유관각산법성능비교적일사신적결론.량개실례적실험결과표명:재14충산법중,GP+GA+LS〖KG-*8〗MU산법(즉재채용GP적모형결구적우화과정중감입채용GA적모형삼수적우화과정,병차재매일연화대대충군중적부분개체진행기우GP적표준변이산자산생린역해적국부수색과정)시목전해결상미분방정조건모문제적최호산법.
Using the population model and the chemical reaction model as examples, the experimental results of building the ordinary differential equation (ODE) models for dynamical systems by using fourteen kinds of algorithms which are based on two traditional searching methods (i.e. the local search algorithm and the simulated annealing algorithm), genetic programming (GP), genetic algorithm (GA) and their combinations are studied in detail. Some new conclusions about the comparisons of the performance of those algorithms are drawn at the end of this paper. The results of both examples show that of the fourteen kinds of algorithms the algorithm called GP+GA+LS〖KG-*8〗MU is the best one in solving the modeling problem of ODE, which is performed by embedding a parameter optimization process using GA into the structure optimization process using GP, and adding local search for some individuals in the population at the end of each generation based on the standard GP mutation operators to generate their neighbor solutions.