基于共享机制的自适应混合遗传算法

共享机制小生境遗传算法常由于保持算法种群的多样性而减缓了全局收敛速度.针对共享机制的这个缺陷,提出了一种基于共享机制的自适应混合遗传算法.将熵的概念引入共享机制,提出了用以度量种群多样性的小生境熵的概念;构造了小生境半径和进化参数(交叉、变异概率)的自适应计算方法;设计了用于增强算法局部搜索寻优能力的扩展突变算子.最后实验表明,该算法对于解决多模态函数优化问题具有很好的全局搜索能力和较快的收敛速度,能够有效避免早熟收敛.     

基于小生境算法和聚类分析的快速收敛遗传算法

摘要：针对遗传算法中存在的早熟收敛和后期收敛速度慢的问题,在讨论种群多样性表示方法和早熟原因的基础上,提出了一种基于小生境技术和聚类分析的遗传算法快速收敛算法．利用小生境技术保持种群的多样性,有效防止早熟收敛．当种群进化到一定程度后,进行聚类分析,从而获得分布在各个极值点附近的聚类区域．在各个聚类中心处,利用局部搜索算法获得极值点;其余个体按照小生境技术在聚类区域外进一步搜索．仿真结果表明,这种算法能够有效地防止早熟收敛,可以极大提高遗传算法的搜索效率,有利于并行实现,并在一定程度上有助于骗问题的解决．     

自适应梯度小生境混合优化算法

通过对梯度法和小生境遗传算法优缺点的分析,提出了一种自适应梯度小生境混合优化算法。小生境算法利用当前种群适应度和种群代数来设计交叉算子和变异算子,保持了种群的多样性,改善全局搜索能力,应用自适应变步长梯度算法的快速寻优特点来减少运行的时间,优化极值精度,加快了收敛速度。对Shubert函数的仿真试验,证明该算法能明显的改善全局搜索能力,加快算法收敛速度。     

基于正交试验法的小生境混合遗传算法

在简单遗传算法应用过程中 ,为了解决早熟现象、收敛缓慢和解的精度差等问题 ,提高遗传算法的优化性能 ,借助正交试验法的全局均衡设计思想和自然界的小生境思想 ,在引入了一些高效进化操作的基础上 ,提出了一种基于正交试验法的小生境混合遗传算法 ,并进行了性能分析和实例研究 .研究结果表明 ,该算法能够有效地增强遗传算法的全局收敛性 ,加快进化速度 ,同时算法稳定性也得到了一定的提高     

基于小生境遗传算法的足球机器人路径规划

将基于共享机制的小生境遗传算法应用到足球机器人路径规划中,对比其他算法说明其在求解多峰值函数优化计算问题时具有时间最优性,并能保持解的多样性,具有很高的全局寻优能力和收敛速度。通过仿真试验证明了小生境遗传算法在路径寻优过程中的有效性和正确性。     

一种用于智能组卷的自适应小生境遗传算法

在采用遗传算法进行智能组卷的过程中,常出现选择策略缺乏多样性保护机制的现象,易出现早熟收敛。为解决智能组卷的早熟收敛问题,提出一种自适应小生境遗传算法。采用小生境技术可提高个体的选择概率,增加个体的多样性选择机率;在保证算法收敛速度的情况下,给出一种一致变异算子,同时调节个体的变异概率和变异范围,提高种群多样性。最后,通过具体实例验证了该算法在较短的组卷时间内,可以实现全局寻优的结果,从而证明该算法的有效性。自适应小生境遗传算法在智能组卷中的应用具有实际意义。     

面向多模态函数优化的自适应小生境遗传算法

为了解决小生境遗传算法不能准确识别小生境的缺陷,以及算法无法有效平衡快速收敛和保持种群多样性的冲突问题,提出一种自适应小生境遗传算法.在算法中,设计一种改进的小生境识别方法来确定小生境范围,引入用于度量种群多样性的小生境熵概念,并利用小生境熵自适应调整进化参数的取值.同时,改进选择、交叉策略,在识别的小生境基础上将交叉分为境外交叉和境内交叉,用于提高算法的全局搜索能力和局部收敛速度.实验表明,算法对于解决多模态函数优化问题具有收敛速度快和计算量小等优点,能够有效避免遗传漂移现象.     

基于改进小生境遗传算法的车牌图像分割

为避免小生境遗传算法存在的早熟和收敛速度慢等问题,结合共轭梯度法,提出了一种改进的小生境遗传算法,能加快收敛速度,改善全局最优解搜索能力。将新算法用于车牌图像分割,进行图像识别。试验表明新算法能解决车牌图像识别率低问题且识别效果好。     

基于小生境-遗传算法优化的新型BP模型

为解决传统BP神经网络模型易陷入局部极小点、网络结构不稳定、收敛速度慢等问题,提出了一个小生境遗传算法优化的BP神经网络模型。该网络模型借助BP神经网络的非线性映射和学习联想能力和小生境遗传算法的搜索能力,利用小生境遗传算法的选择、交叉、变异及小生境淘汰等操作,来对BP神经网络的初始权值和阈值进行优化,同时使用BP算法来训练该模型,从而有效地解决了网络初值不合理的问题,提高了网络收敛速度、稳定性。实验证明：与传统方法相比,该模型具有很强的可行性和有效性。     

小生境遗传模糊PID控制在皮带秤中的应用

采用模糊PID方法控制流量恒定,并结合遗传算法优化PID参数.引入小生境技术,克服了传统遗传算法早熟收敛和后期收敛速度慢以及收敛效率低的缺点,同时对遗传算子也进行了改进,获得理想的全局寻优效果.     

用于一类函数全局优化问题的混合遗传算法

为了克服传统遗传算法收敛速度缓慢且易于收敛到局部最优解的缺点,该文将遗传算法与传统的局部搜索方法相结合,采用新的交叉变异准则,提出一种新型的混合遗传算法。该算法可以很好地处理一类带上下界约束的全局优化问题,具有很强的全局寻优能力。数值实验表明,该算法的计算结果明显优于传统遗传算法。     

基于混合遗传算法的自动组卷问题的研究

针对遗传算法(GA)容易出现未成熟收敛和进化后期计算效率低的问题,提出了一种基于混合遗传算法(HGA)的智能组卷算法.将自适应遗传算法(AGA)与位爬山法相结合,提高组卷性能.在进化前期采用AGA进行全局寻优,增强GA的收敛速度同时避免GA的未成熟收敛.在进化后期启动位爬山法增强AGA的局部搜索能力.试验结果表明,HGA相对于AGA在有效性、稳定性和计算效率三方面都有较大提升,更能有效解决自动组卷问题,具有较好的使用性能和实用性.     

An approach based on hybrid genetic algorithm applied to image denoising problem

An approach based on hybrid genetic algorithm (HGA) is proposed for image denoising. In this problem, a digital image corrupted by a noise level must be recovered without losing important features such as edges, corners and texture. The HGA introduces a combination of genetic algorithm (GA) with image denoising methods. During the evolutionary process, this approach applies some state-of-the-art denoising methods and filtering techniques, respectively, as local search and mutation operators. A set of digital images, commonly used by the scientific community as benchmark, is contaminated by different levels of additive Gaussian noise. Another set composed of some Satellite Aperture Radar (SAR) images, corrupted with a multiplicative speckle noise, is also used during the tests. First, the computational tests evaluate several alternative designs from the proposed HGA. Next, our approach is compared against literature methods on the two mentioned sets of images. The HGA performance is competitive for the majority of the reported results, outperforming several state-of-the-art methods for images with high levels of noise.     

Fuzzy adaptive genetic algorithms: design,taxonomy, and future directions

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature convergence problem and improve the final results. One of the adaptive approaches are the adaptive parameter setting techniques based on the use of fuzzy logic controllers, the fuzzy adaptive genetic algorithms (FAGAs). In this paper, we analyse the FAGAs in depth. First, we describe the steps for their design and present an instance, which is studied from an empirical point of view. Then, we propose a taxonomy for FAGAs, attending on the combination of two aspects: the level where the adaptation takes place and the way the Rule-Bases are obtained. Furthermore, FAGAs belonging to different groups of the taxonomy are reviewed. Finally, we identify some open issues, and summarise a few new promising research directions on the topic. From the results provided by the approaches presented in the literature and the experimental results achieved in this paper, an important conclusion is obtained: the use of fuzzy logic controllers to adapt genetic algorithm parameters may really improve the genetic algorithm performance. This research has been supported by DGICYT PB98-1319.     

基于HGA算法的Rough集理论在故障诊断中的应用

知识约简问题是粗集理论的一个核心问题,文章提出了一种基于混合遗传算法的相对约简算法,把模拟退火融入到遗传算法中形成混合遗传算法,提高了遗传算法的优化效率,并在此基础上寻求最小条件属性集及最小属性值约简,论文最后以某导弹测控系统配电分系统故障诊断为例,证明该算法是一种行之有效的约简算法,从而为导弹系统的故障诊断提供了一条新思路.     

Hybrid genetic algorithm approach for precedence-constrained sequencing problem

The objective of precedence-constrained sequencing problem (PCSP) is to locate the optimal sequence with the shortest traveling time among all feasible sequences. Various methods for effectively solving the PCSP have been suggested. This paper proposes a new concept of hybrid genetic algorithm (HGA) with adaptive local search scheme in order that the PCSP should be effectively solved. By the use of the adaptive local search scheme, the local search is automatically adapted into the loop of genetic algorithm. Two types of the PCSP are presented and analyzed to compare the efficiency among the proposed HGA approach and other competing conventional approaches. Finally, it is proved that the proposed HGA approach outperforms the other competing conventional approaches.     

基于粒子群位移转移的混合遗传算法及其应用

基于粒子群位移转移的思想,改变遗传算法的变异规则,提出了一种新的混合遗传算法。利用3个benchmark函数测试了新的混合算法的性能,并将测试结果与标准遗传算法进行了比较。提出了一种多阶段半方差投资选择模型,并将混合算法应用在多阶段半方差投资选择问题的求解上。     

Convergence of nomadic genetic algorithm on benchmark mathematical functions

Nomadic genetic algorithm is a type of multi-population migration based genetic algorithm that gives equal importance to low fit individuals and adaptively chooses its migration parameters. It has been applied to several real life applications and found to perform well compared to other genetic algorithms. This paper exploits the working of nomadic genetic algorithm (NGA) for benchmark mathematical functions and compares it with the standard genetic algorithm. To compare its performance with standard GA (SGA), the prominent mathematical functions used in optimization are used and the results proved that NGA outperforms SGA in terms of convergence speed and better optimized values.     

求解0-1背包问题的改进混合遗传算法

针对一种混合遗传算法所采用的贪心变换法的不足,给出了一种改进的贪心修正法;并基于稳态复制的策略,对遗传算法的选择操作进行改进,给出了随机选择操作。在此基础上,提出了一种改进的混合遗传算法,并将新算法用于解决大规模的0-1背包问题,通过实例将新算法与 HGA 算法进行实验对比分析,并研究了变异概率对新算法性能的影响。实验结果表明新算法收敛速度快,寻优能力强。     

A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots to allow the overlapping of operations between successive machines in a multi-stage production system. The use of sublots usually results in substantially shorter job completion times for the corresponding schedule. A new genetic algorithm (NGA) is proposed for an n-job, m-machine, lot-streaming flow shop scheduling problem with equal size sublots and limited capacity buffers with blocking in which the objective is to minimize total earliness and tardiness penalties. NGA replaces the selection and mating operators of genetic algorithms (GAs), which often lead to premature convergence, by new operators (marriage and pregnancy operators) and also adopts the idea of inter-chromosomal dominance and individuals’ similarities. Extensive computational experiments have been conducted to compare the performance of NGA with that of GA. The results show that, on the average, NGA outperforms GA by 9.86 % in terms of objective function value for medium to large-scale lot-streaming flow-shop scheduling problems.