【作者】 刘大莲；

【导师】 王宇平；

【作者基本信息】 西安电子科技大学 ， 运筹学与控制论， 2004， 硕士

【摘要】 进化算法是模拟生物界的进化过程而产生的一种现代优化方法，作为一种有效的随机搜索方法，在优化方法中具有独特的优越性，有着非常重要的意义和及其广泛的应用。传统优化方法对目标函数解析性质要求较高，进化算法不需要目标函数的导数信息，具有隐式并行性，所以常用于解决一些复杂的、大规模的、非线性、不可微的优化问题。首先，本文对无约束优化问题提出了一个新的进化算法，这种算法利用平滑技术构造了一个新的适应度函数，并适时结合一维搜索去解决无约束优化问题。新的适应度函数具有有效去除部分局部极小点的优越性能，使得整个算法大大减小了陷入局部最优的可能性；而新的杂交算子和适时一维搜索使得算法更迅速有效的找到全局最优。其次，把原约束优化问题转换为只有两个目标函数的多目标优化问题，并针对新的模型设计了新的遗传算子，在此基础上对新的模型设计了一个新的遗传算法。通过求解多目标优化问题而得到约束优化问题的最优解。数值试验表明算法是有效的。更多还原

【Abstract】 Evolutionary algorithms are new kinds of modern optimization algorithms that are inspired by principle of nature evolution. As new kinds of random search algorithms, they have some advantages over the traditional optimization algorithms, and are of the great importance and have a wide range of applications. The traditional optimization algorithms usually have strict limitations on the functions such as their differentiability, however, evolutionary algorithms do not require the differentiability of the functions and have parallel property. Therefore, they are often be used to solve some complex, large scale, nonlinear and non-differentiable optimization problems.First, a new evolutionary algorithm for unconstrained optimization problems is proposed in this paper. In the proposed algorithm a new fitness function based on smoothing technique is designed, and a novel line search scheme is integrated into the algorithm design to improve the efficiency of the algorithm. The new fitness function has the advantages that the many local optimal solutions can be removed by using this fitness function. As a result, it is less possible for the proposed algorithm to trap into the local optimal solutions. Moreover, the new crossover operator designed in this paper and the new line search scheme can make the proposed algorithm find the global optimal solutions more quickly.Second, the constrained optimization problem considered is transformed into a multi-objective optimization problem with two objectives, and new genetic operators are designed for the multi-objective optimization problem model. Based on these a new genetic algorithm is proposed for the new model. By solving the multi-objective optimization problem we can get the optimal solutions for the constrained optimization problem. The simulation results show the effectiveness of the proposed algorithm.更多还原