摘要
提出了水环境优化问题的一种新方法——蚁群加速遗传算法,给出了实施该算法的详细步骤。并对新方法的收敛性和全局优化性进行了理论和实例分析,在污水处理模型的参数识别问题中,新方法得到了精度较高的全局最优解。新方法具有精度高、速度快和鲁棒性强等特点,是一种既可以较大概率搜索全局最优解,又能进行局部细致搜索的较好的非线性优化方法,可广泛应用于各种水环境优化问题中。
In order to improve convergence speed and calculation accuracy of genetic algorithm, a new method, Ant Colony Accelerating Genetic Algorithm (ACAGA), and its detailed steps of solution are developed for sloving water environment optimization. The crossover and mutation operators of real code genetic algorithm are put into the ant colony algorithm, and the parameter values automatically satisfy the restrict condition. With the shrinking of searching range, the method gradually directs to optimal result by the excellent individuals obtained by real code genetic algorithm (RGA) embedding with super individuals searching operator, the convergence and global optimization of ACAGA is discussed theoretically and practically, and its high precision on global optimization is ascertained over such parameters as sewage disposal model ones. Compared with RGA and the conventional optimization methods, ACAGA remarkably improves convergence speed and calculation accuracy. It proved to be a good nonlinear optimal method that can search both global solution and local one in greater probability, and could be applied to various water environment optimization issues.
出处
《水电能源科学》
2003年第4期42-45,共4页
Water Resources and Power
基金
国家重点基础研究发展规划项目(G1999043605)。
