利用优化的遗传算法解决饲料配方设计问题

Application of optimization genetic algorithm in feed formulation design

利用改进和优化传统遗传算法的选择策略、搜索空间,自适应调整交叉率和变异率提高了计算效率,并在遗传进化过程中用优秀个体群来逐步缩小搜索空间,提出了求解饲料配方设计问题的一种改进方法(GA+).应用该方法对3个经典非线性测试函数进行了仿真,在收敛速度和全局优化方面好于现有的遗传算法.结果表明,GA+较好地保持了种群的多样性,精度高、收敛速度快,对求解饲料配方设计问题非常有效.

An improved method （GA ＋ ） for feed formulation design is presented. Computational efficiency is increased by improving and optimizing the traditional genetic algorithm selection strategy, search space, adaptive crossover rate and mutation rate, and adopting excellent individual group to reduce the search space in genetic evolution process. Application of the method to emulation experiment with three classic nonlinear functions, it shows that the convergence speed and global optimization are better than the ones in traditional genetic algorithm. The result shows that GA ＋ does well in maintaining the population diversity, high precision, fast convergence speed, and is very effective in solving design problems of feed formulation.