遗传算法的全局动力学形态分析

目前，对遗传算法的运行机理分析大都集中在算法的极限收敛性等问题，对算法的全局动力学形态研究较少．从一个具有代表性的、简化的2—bit问题入手，可以对遗传算法中常用的各种进化算子及其组合进行形式化描述，从而全面分析GA的全局动力学形态．针对各种参数的选取，分别建立了4个数学模型．通过分析这些模型中各个不动点的吸引性，揭示出不同进化算子对动力学形态的影响．对于这个问题，证明了算法的全局收敛性．并指出，当存在两个被此竞争的局部极值点时，模型中只有两个吸引点和一个鞍点(或排斥点)，不存在其他的不动点或周期点．算法的收敛结果完全由初始条件处于状态空间中的位置所决定，相应的收敛区域的比例完全由模型的参数决定．     

四点三线遗传算法求解旅行商问题

为解决NP完全的旅行商问题,提出一种四点三线遗传算法。该算法特色在两阶段策略,第一阶段是变异算子优化,将汉密尔顿环中所有大于两点的内部路径倒置,并用新极值代替原极值。第二阶段是四点三线优化,将汉密尔顿环分为n个四点三线局部路径并将每个局部路径转化为最优局部路径,将所有局部路径长度求和除以1/3。交叉算子结束后,如子代含有重复位点,将未交叉部分重复位点与交叉部分重复位点对应的父代等位点交换。通过将该算法与传统遗传算法及只进行第一步优化的遗传算法进行比较,采用TSPLIB数据库实例数据,证明该算法有更高的执行效率,有更强的收敛性,适合寻找最短TSP路径。     

基于改进遗传算法的优化计算

针对经典遗传算法在优化计算中存在的弊端,提出改进遗传算法。该算法考虑了优化问题的全局性要求—结合区间压缩方法,而这往往比局部最优理论和方法困难的多;同时通过对变异算子改进,对遗传算法早熟收敛性方面得到有效控制,最后,给出算法的收敛性证明及收敛性准则。实验表明该算法是有效的。     

自适应遗传算法的改进与应用

针对遗传算法易出现早熟现象，通过对标准遗传算法和自适应遗传算法的分析研究，本文对自适应遗传算法进行了改进。即在保留以往自适应遗传算法优点的同时，设计了与种群个体分布及种群规模的波动情况相关的自适应遗传算子。实验结果表明：该算法不易陷入局部极值，收敛速度快。     

一种多模态优化的小生境遗传算法

针对目前多模态优化存在无法找到全部局部极值解的问题,提出了一种改进的小生境遗传算法．该算法在基于淘汰相似机制的小生境遗传算法中引入了预选择机制,并对自适应交叉概率算子和变异概率算子进行了改进,根据群体适应度值的大小来动态调整个体的交叉概率和变异概率大小,并将该算法用于Shubert函数的求解,实验结果表明该方法较之前的小生境遗传算法能够有效的搜寻出全部局部极值,并具有较快的搜索速度．同时,该方法在其他的多峰函数求解上具有通用性．     

混沌蜜蜂双种群进化遗传算法

利用混沌运动的遍历性、随机性和规律性等特点,提出混沌蜜蜂双种群进化遗传算法。该算法在基于蜜蜂双种群进化遗传算法的基础上,利用混沌优化进行改善初始种群质量和利用混沌退化变异算子代替常规算法中的变异算子,避免搜索过程陷入局部极值。实验结果表明,该算法计算速度快、收敛性好,提高了常规遗传算法的收敛速度和优化效果。     

遗传算法理论综述

通过遗传算法的描述,介绍了有关遗传算法理论的研究现状,如:编码方法、适应值函数、遗传算子、参数的选择、数学基础、欺骗问题、收敛性分析、并行计算、局部改进和混合方法等。尽管遗传算法理论基础的不完善使其在应用中还存在某些问题,但通过遗传算法的理论研究将有助于遗传算法理论及其应用的发展。     

基于改进遗传算法的城市交通动态最优路径求解

针对传统遗传算法在交通诱导系统中求解最优路径问题中存在早熟收敛,易陷入局部极值点以及求得的最优路径缺乏实时性的问题,在模型中加入了实时交通信息,引入了一种新的带染色体交叉控制策略的改进遗传算法,配合单点交叉算子,消除了传统遗传算法中早熟收敛的不足,并使所求最优路径更加贴近实时的交通状态,切实达到诱导目的,提高整体路网的运行效率。     

基于改进遗传算法的舰船路径规划

遗传算法在解决非线性问题上具有良好的适用性,但是也存在着收敛性慢和局部最优解的缺陷,并且在实际应用中缺乏特定知识的利用.针对舰船路径规划的特点,对标准遗传算法进行了改进和优化,采用基于坐标的一维编码方式,设计了插入算子、删除算子、平滑算子和扰动算子,提高了进化效率.计算机仿真结果表明,该算法在收敛速度和输出全局最优解的概率相对于标准遗传算法都有了显著提高.     

改进遗传算法全局收敛性分析

传统的遗传算法大多数没有给出收敛性准则。一类新的改进的遗传算法被提出,该算法即考虑了优化问题的全局性要求——每一步构造一个新函数,而这往往却比局部最优理论和方法困难得多;同时通过对选择算子的改进,对遗传算法后期进化缓慢问题得到了有效控制,最后给出了算法的收敛性证明以及收敛性准则。实例证明该算法是有效的。     

Structural topology and element sizing design optimisation of tall steel frameworks using a hybrid OC–GA method

This paper presents a hybrid optimisation method in which a local search operator based on a rigorously derived optimality criteria (OC) technique is embedded in the framework of a genetic algorithm (GA). The GA framework is particularly useful in the global exploration for optimal topologies, while the OC technique serves as a local search operator for efficient element sizing optimisation of given topologies. The hybrid OC–GA method was developed to strike a balance between the exploration of global search algorithms and the exploitation of efficient local search methods so as to make the hybrid method suitable for optimising tall building structures involving a large number of structural elements. The applicability and efficiency of the hybrid OC–GA method were tested with two 40-storey steel frameworks. The results show that the hybrid method can generate superior designs to pure GA while exhibiting rapid and smooth convergence, suggesting its great potential for optimising both structural form and element size of practical tall building structures.     

基于改进遗传算法的加权模糊C均值聚类算法

针对模糊C均值(FCM)聚类算法具有初始聚类中心敏感和容易陷入局部最优的问题,提出了一种基于改进遗传算法(GA)的加权模糊c均值聚类算法,采用高斯变异算子,提高了遗传算法在每个峰值附近的局部搜索能力,用基于复相关系数的加权欧式距离代替欧式距离,改进了FCM算法的聚类目标函数.用改进的算法对国际标准测试数据Iris进行测试,实验结果表明改进后的算法具有更好的稳定性和健壮性,提高了聚类的效果.     

求解相容非线性方程组的拟牛顿法和混合计算智能算法

本文针对变量数与方程数不一致的相容非线性方程组(CNLE),先给出拟牛顿(QN)法.针对该算法的局部收敛性容易导致求解失败,通过在遗传算法(GA)中嵌入QN算子,并定义适当的适应度,从而得到结合GA和QN法两者长处,既有较快收敛性,又能以较大概率求解CNLE的混合计算智能算法.计算结果表明本文方法显著优于GA和QN法.     

A new hybrid genetic algorithm for job shop scheduling problem

Job shop scheduling problem is a typical NP-hard problem. To solve the job shop scheduling problem more effectively, some genetic operators were designed in this paper. In order to increase the diversity of the population, a mixed selection operator based on the fitness value and the concentration value was given. To make full use of the characteristics of the problem itself, new crossover operator based on the machine and mutation operator based on the critical path were specifically designed. To find the critical path, a new algorithm to find the critical path from schedule was presented. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed and its convergence was proved. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm.     

一种基于遗传算法的进化计算模型

提出一种基于遗传算法的进化计算模型(ECM).在ECM的种群中,每个成员都根据其适应度值不同程度地影响着种群的进化.ECM定义了个体对进化的影响因子,并以个体的影响因子为参数定义了个体的形成算子.分析表明,ECM是采用算术交叉算子的两父辈遗传算法以及采用频率扫描交叉算子的多父辈交叉遗传算法的推广,形成操作是父代群体编码的凸组合.实验研究显示,ECM具有比经典遗传算法更强的优化计算功能.     

基于成长算子的改进遗传算法及仿真

模拟生物界成长发育过程,加入成长算子对遗传算法框架进行改进,形成新的算法框架-成长遗传算法(growth GA)．该算法能够克服简单遗传算法寻优速度较慢、局部搜索能力较弱的缺点．利用爬山法局部搜索能力强的特点,给出成长算子的一种具体实现,并证明加入成长算子不改变算法收敛性．与简单遗传算法和确定性拥挤遗传算法的对比函数优化实验证明:成长遗传算法有利于兼顾寻优速度和收敛精度．     

基于牛顿法和遗传算法求解非线性方程组的混合计算智能方法

本文针对牛顿法的局部收敛性而容易导致求解失败，先讨论在全局空间搜索解的非线性方程组遗传算法（ＧＡ）。然后针对ＧＡ收敛慢，通过定义牛顿算子，适应度函数和选择算子，从而得到结合ＧＡ和牛顿法两者长处，既有较快收敛性，又能以较大概率求解非线性方程组的混合计算智能算法。数值计算表明本文方法显著优于牛顿法和ＧＡ。     

用于TSP的自适应贪婪GA算法

TSP问题是一个典型的组合优化问题,很多现实生活中的问题都可以归结为TSP问题,GA算法是一种典型的优化算法。通过对GA算法要点的分析,提出了一种自适应贪婪GA算法,以解决TSP问题。自适应适应度函数的各种定义、定理,确保了算法的正确性。通过平均复制的方法进行选择操作,使得算法不会过早地陷入局部最优。通过建立基于哈密顿回路的双向环贪婪插入算子进行交叉操作,确保了算法收敛的高效性。最后通过实例的计算分析及与传统GA算法的比较,说明了所提出的自适应贪婪GA算法在TSP研究中能够更好地发挥作用。     

Microgenetic algorithms as generalized hill-climbing operators forGA optimization

We investigate the potential of a microgenetic algorithm (MGA) as a generalized hill-climbing operator. Combining a standard GA with the suggested MGA operator leads to a hybrid genetic scheme GA-MGA, with enhanced searching qualities. The main GA performs global search while the MGA explores a neighborhood of the current solution provided by the main GA, looking for better solutions. The MGA operator performs genetic local search. The major advantage of MGA is its ability to identify and follow narrow ridges of arbitrary direction leading to the global optimum. The proposed GA-MGA scheme is tested against 13 different schemes, including a simple GA and GAs with different hill-climbing operators. Experiments are conducted on a test set including eight constrained optimization problems with continuous variables. Extensive simulation results demonstrate the efficiency of the proposed GA-MGA scheme. For the same number of fitness evaluations, GA-MGA exhibited a significantly better performance in terms of solution accuracy, feasibility percentage of the attained solutions, and robustness     

An enhanced GA technique for system training and prognostics

The commonly used genetic algorithm (GA)-based methods have some shortcomings in applications such as time-consuming and slow convergence. A novel enhanced genetic algorithm (EGA) technique is developed in this paper to overcome these problems in classical GA methods so as to provide a more efficient technique for system training and optimization. Two approaches are proposed in the EGA technique: Firstly, a novel group-based branch crossover operator is suggested to thoroughly explore local space and speed up convergence. Secondly, an enhanced MPT (Makinen-Periaux-Toivanen) mutation operator is proposed to promote global search capability. The effectiveness of the developed EGA is verified by simulations based on a series of benchmark test problems. The EGA technique is also implemented to train a neural-fuzzy predictor for real-time gear system monitoring. Test results show that the branch crossover operator and enhanced MPT mutation operator can effectively improve the convergence speed and global search capability. The EGA technique outperforms other related GA methods with respect to convergence speed and global search capability.