一种选择性神经网络集成构造方法

提出了一种选择性神经网络集成构造方法,在训练出个体神经网络之后,使用遗传算法部分网络来组成神经网络集成。理论分析和实验结果表明,与传统的使用所有体网络的方法相比,该方法能够取得更好的效果。     

基于遗传算法的非线性系统模型参数估计

针对一性系统系统模型的多样性,提出适用于多种非线性模型的基于遗传算法的参数估计方法,并以多种非线性模型为例作了仿真研究,结果表明,遗传算法是非线性系统模型参数估计的有效工具。     

智能算法在机器人路径规划中的应用研究

移动机器人技术研究中的一个重要领域是路径规划技术。综述了智能算法在移动机器人路径规划技术中的发展现状,指出了各种方法的优点与不足。最后对移动机器人路径规划技术的发展趋势进行了展望。     

遗传算法的研究与应用

遗传算法(Genetic Algorithms,GA)是一种模拟自然选择和遗传机制的寻优程序,遗传算法本身固有的并行处理性和开放性,使得它在优化识别的效率非常之高,而且受到越来越广泛的研究。然而,遗传算法自身也有一些缺点。论文研究了遗传算法的起源,发展,原理及自身的缺点。以便对这种算法深入了解,灵活应用,以及做进一步的研究开发。     

求解函数优化问题的遗传算法

从遗传算法的编码、遗传算子等方面剖析了遗传算法求解无约束函数优化问题的一般步骤,并以一个实例说明遗传算法能有效地解决函数优化问题。     

带种族遗传算法的神经网络辨识器及其应用

为了解决油田动态系统建立数字模型难和模型训练难的问题,以多层动态前向网络为模型框架,给出了一种新的油田系统神经网络辩识器,它对上述系统具有通用性。为了训练该辩识器和避免极值问题,模拟生物种族进化机制,设计了新的种族遗传算法。该算法是由改进的带共享机制的遗传算法、启发式山峰聚类算法和新的Powell算法有机结合构成的新型混合算法。应用表明,种族遗传算法具有极强的全局和局部搜索能力,这确保了上述神经网络辩识器具有很高的预测精度,平均相对误差在1%以内。     

改进的RBF神经网络在磨矿指标预测中的应用

针对选矿生产过程中磨矿生产率和磨矿浓度、介质填充率和料球比3个指标之间的关系,分析了磨矿生产率对磨矿过程产品质量和工作效率的影响,提出了磨矿过程预测模型,采用了模糊聚类与改进的遗传算法相结合训练RBF神经网络的新方法。仿真结果表明,该模型可以快速准确地预报生产状况,对于企业进行实时生产控制具有较高的实用价值。     

Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a new multi-objective shuffled frog-leaping algorithm (MOSFLA) is introduced for the first time to search locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSFLA performs better than the above genetic algorithms, especially for the large-sized problems.     

Interval optimization of rotor-bearing systems with dynamic behavior constraints using an interval genetic algorithm

A new interval optimization algorithm is presented in this paper. In engineering, most optimization algorithms focus on exact parameters and optimum objectives. However, exact parameters are not easy to be manufactured to because of manufacturing errors and expensive manufacturing cost. To account for these problems, it is necessary to estimate interval design parameters and allowable objective error. This is the first paper to propose a new interval optimization algorithm within the context of Genetic Algorithms. This new algorithm, the Interval Genetic Algorithm (IGA), can neglect interval analysis and determines the optimum interval parameters. Furthermore, it can also effectively maximize the design scope. The optimizing ability of the IGA is tested through the interval optimization of a two-dimensional function. Then the IGA is applied to rotor-bearing systems. The results show that the IGA is effective in deriving optimal interval design parameters within the allowable error when minimizing shaft weight and/or transmitted force of rotor-bearing systems.     

遗传算法结合蚁群算法电力系统故障浅析

根据蚁群算法与遗传算法的特性,提出了与遗传算法混合的蚁群算法（G3A）：由遗传算法生成初始信息素分布,在蚁群算法寻优中,利用蚁群算法信息素轨迹更新求精确解,保持了遗传算法的全面搜索能力,从而获得在时间效率与精解效率都更优的一种新的启发式方法。