摘要
自 2 0世纪 80年代以来 ,遗传算法在工程优化领域获得了广泛的应用 .遗传算法对数学模型要求不高 ,具有一定的隐性并行性 ,能同时在搜索空间进行大范围搜索 ,因而不易陷入局部最优解 .然而 ,在实际应用中发现 ,仅有交叉算子和变异算子的传统遗传算法 ,局部搜索能力不强 ,容易出现种群早熟 ,进化结束时往往收敛到最优点附近而达不到全局最优点 .为此借鉴了传统优化方法中的搜索技术 ,开发了几种算子用以强化遗传算法的局部搜索能力 .算例表明 ,这几种算子能提高遗传算法的搜索性能 。
Since the 1980s,Genetic Algorithm has been widely employed in the field of engineering optimization.Genetic Algorithm does not demand much more for mathematical model.With the merit of potential parallelism,it can search simultaneously on a large scale in the problem space,so it is not easy to be trapped to partial optimal solution.However,the practice of engineering optimization demonstrates that traditional Genetic Algorithm,only with crossover operator and mutation operator,has no strong searching ability.The premature often occurs.When the evolution is over,it often converges to the vicinity of overall solution and can not reach the ultimate solution.In the suggestion from traditional searching techniques,this paper exploits several operators to strengthen partial searching ability of traditional Genetic Algorithm.The example shows not only efficiency but also the quality of result is improved.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2001年第12期1391-1394,共4页
Journal of Tongji University:Natural Science
