精英多父体杂交算法(elite multi-parent crossover algorithm,EMCA)被广泛应用于众多优化领域,如路由优化、选址优化和路径优化等。但是,该算法中用于多父体重组的系数向量的生成方法,目前国内外还没有深入的研究。为了提高EMCA算法的...精英多父体杂交算法(elite multi-parent crossover algorithm,EMCA)被广泛应用于众多优化领域,如路由优化、选址优化和路径优化等。但是,该算法中用于多父体重组的系数向量的生成方法,目前国内外还没有深入的研究。为了提高EMCA算法的收敛效率,首先分析了EMCA算法中合格系数向量的生成方法与效率,发现当参与杂交的父代染色体个数超过13时,系数向量的生成效率急剧下降为0。但是在EMCA算法的实际应用中,为了让后代继承更多的优秀父代基因,参与杂交的父代染色体个数往往大于13。为了解决该问题,提出了依经验概率密度曲线生成系数向量的方法(empirical probability density curve,EPDC),并对EPDC与参与杂交的父代染色体个数进行建模和模型验证。最后用标准数据集上的6个测试函数对EPDC的有效性进行实验验证,结果表明:EPDC可将EMCA算法的平均收敛效率提高3~4倍。展开更多
Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value ...Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value to the one that is better than any solution lying beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with six other evolutionary based methods, using 12 benchmark problems from the literature, it shows very encouraging performance. T-tests have been applied in this research to show if there is any statistically significance differences between the algorithms. A study has also been carried out in order to show the effect of each component of the proposed algorithm.展开更多
文摘精英多父体杂交算法(elite multi-parent crossover algorithm,EMCA)被广泛应用于众多优化领域,如路由优化、选址优化和路径优化等。但是,该算法中用于多父体重组的系数向量的生成方法,目前国内外还没有深入的研究。为了提高EMCA算法的收敛效率,首先分析了EMCA算法中合格系数向量的生成方法与效率,发现当参与杂交的父代染色体个数超过13时,系数向量的生成效率急剧下降为0。但是在EMCA算法的实际应用中,为了让后代继承更多的优秀父代基因,参与杂交的父代染色体个数往往大于13。为了解决该问题,提出了依经验概率密度曲线生成系数向量的方法(empirical probability density curve,EPDC),并对EPDC与参与杂交的父代染色体个数进行建模和模型验证。最后用标准数据集上的6个测试函数对EPDC的有效性进行实验验证,结果表明:EPDC可将EMCA算法的平均收敛效率提高3~4倍。
文摘Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value to the one that is better than any solution lying beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with six other evolutionary based methods, using 12 benchmark problems from the literature, it shows very encouraging performance. T-tests have been applied in this research to show if there is any statistically significance differences between the algorithms. A study has also been carried out in order to show the effect of each component of the proposed algorithm.
