适应值共享拥挤遗传算法

保持遗传算法在演化过程中的种群多样性,是将遗传算法成功应用于解决多峰优化问题和多目标优化问题的关键。适应值共享遗传算法和拥护遗传算法分别从不同角度改善了遗传算法的搜索能力,是寻找多个最优解的常用算法。将这两种算法的优点加以结合,提出适应值共享拥护遗传算法。数值测试结果表明,该算法比标准适应值共享遗传算法和确定性拥挤遗传算法具有更强的搜索能力。     

对适应值共享遗传算法的分类及评价

针对多峰函数优化问题先后出现了一系列适应值共享类的遗传算法.这些算法都需要事先提供某种信息.本文基于事先提供信息的区别提出了一种新的适应值共享类遗传算法的分类方法,并通过一个复杂的标准测试问题对这些算法进行了比较和评价,结果表明在各种算法中,清除算法、动态小生境共享算法和新聚类适应值共享算法具有较高的搜索能力和优化速度.本文的工作对于这些适应值共享类遗传算法的应用和进一步改进具有指导意义.     

基于适应值共享的多生境排挤遗传算法

针对优化多模函数时单纯使用共享和排挤机制的遗传算法所存在的缺陷,提出了基于适应值共享的多生境排挤遗传算法。基本思想是：按照共享的思想在对个体的适应值进行调整的同时,将排挤选择和相似个体中适应度最差个体被替换的策略分别应用于选择算子和群体的进化中。理论分析和数值实验表明,该算法很好地维持了种群多样性,对于各类多峰函数具有较强的搜索能力。     

自适应调整峰半径的适应值共享遗传算法

适应值共享遗传算法需要事先给出解空间中峰的数目或峰的半径,这对于某些问题来说是有困难的.针对这类问题,提出将峰的半径作为决策变量,对其进行编码并放入染色体中参与演化过程,利用遗传算法的优化能力在对问题进行优化的同时对个体的峰半径进行自适应调整.用所提出的方法对多个标准测试问题的优化结果表明,采用自适应峰半径调整方法的适应值共享遗传算法有很强的多峰搜索能力.     

自适应模糊聚类小生境遗传算法

提出了基于峰半径自适应调整和模糊相似聚类的小生境遗传算法。其基本思想是:在演化过程中;将峰半径作为决策变量的一部分参与染色体的编码;在对问题进行优化的同时对个体的峰半径进行自适应调整;在聚类过程中;通过对模糊相似度的调节来控制小生境的数目;以避免找到无效的极值点。理论分析和数值实验表明;该算法无需事先确定小生境的数目和半径;对于各类多峰函数具有较强的搜索能力。     

遗传算法选择操作及MATLAB实现

本文系统分析了贯穿遗传算法各步骤的选择操作。采用正交优化和随机产生初始解相结合方法，保证初始解多样性和均匀性。改进轮盘赌法的执行效率并和精华保存法结合，选出复制对象，克服了局部收敛。并结合一个多峰函数给出其完整matlab程序。仿具试验表明程序有理想的收敛速率和能够对函数进行合局寻优。     

基于自适应K均值聚类的小生境遗传算法

在自适应小生境遗传算法的基础上,该文提出自适应K—均值聚类适应值共享小生境遗传算法。这种算法将聚类分析、自适应技术有机地结合起来,并且对于通常的K——均值聚类方法做了改进,即引进了一个最小聚类距离,通过调节最小聚类距离控制收敛到的小生境的数目,避免找到无效的极值点。这种算法不仅无需事先确定生境的具体数目和生境半径的大小,而且计算量小,搜索效率较高。     

动态聚类适应值共享遗传算法在散射计风矢量反演中的应用

风矢量反演是散射计数据处理的核心内容,传统风矢量反演算法的设计过多依赖于目标函数的具体分布形态。以SeaWinds散射计为例,根据风矢量反演的多解问题和模糊解特性,设计了一种基于动态小生境遗传算法的风矢量反演算法。利用部分L2A和相应L2B数据对该算法进行了验证。结果表明该算法在无需任何目标函数先验知识的条件下能够取得较好的反演结果。     

用适应值激励机制提出遗传算法的效率

分析了传统遗传算法作为函数优化器在宏观进化机制上的局限性,讨论了群体的可进化性在函数优化中的作用。在此基础上提出在遗传算法中引入适应值激励机制,用它来动态地提高群体的可进化性。数值实验表明,带有适应值激励机制的改进遗传算法的搜索效率得到很大提高。     

基于区间适应值灰度的交互式遗传算法

针对交互式遗传算法缺乏衡量评价的不确定性问题,采用区间数评价进化个体适应值,利用灰度衡量评价的不确定性。通过区间适应值的灰度分析,提取反映种群进化分布的信息,给出进化个体的自适应交叉和变异概率。应用于服装进化设计系统的分析结果表明,该算法可有效缓解人的疲劳,提高优化效率。     

Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization

Evolutionary computation (EC) paradigm has undergone extensions in the recent years diverging from the natural process of genetic evolution to the simulation of natural life processes exhibited by the living organisms. Bee colonies exemplify a high level of intrinsic interdependence and co-ordination among its members, and algorithms inspired from the bee colonies have gained recent prominence in the field of swarm based metaheuristics. The artificial bee colony (ABC) algorithm was recently developed, by simulating the minimalistic foraging model of honeybees in search of food sources, for solving real-parameter, non-convex, and non-smooth optimization problems. The single parameter perturbation in classical ABC resulted in fairly commendable performance for simple problems without epistasis of variables (separable). However, it suffered from narrow search zone and slow convergence which eventually led to poor exploitation tendency. Even with the increase in dimensionality, a significant deterioration was observed in the ability of ABC to locate the optimum in a huge search volume. Some of the probable shortcomings in the basic ABC approach, as observed, are the single parameter perturbation instead of a multiple one, ignoring the fitness to reward ratio while selecting food sites, and most importantly the absence of environmental factors in the algorithm design. Research has shown that spatial environmental factors play a crucial role in insect locomotion and foragers seem to learn the direction to be undertaken based on the relative analysis of its proximal surroundings. Most importantly, the mapping of the forager locomotion from three dimensional search spaces to a multidimensional solution space calls forth the implementation of multiple modification schemes. Based on the fundamental observation pertaining to the dynamics of ABC, this article proposes an improved variant of ABC aimed at improving the optimizing ability of the algorithm over an extended set of problems. The hybridization of the proposed fitness learning mechanism with a weighted selection scheme and proximity based stimuli helps to achieve a fine blending of explorative and exploitative behaviour by enhancing both local and global searching ability of the algorithm. This enhances the ability of the swarm agents to detect optimal regions in the unexplored fitness basins. With respect to its immediate surroundings, a proximity based component is added to the normal positional modification of the onlookers and is enacted through an improved probability selection scheme that takes the T/E (total reward to distance) ratio metric into account. The biologically-motivated, hybridized variant of ABC achieves a statistically superior performance on majority of the tested benchmark instances, as compared to some of the most prominent state-of-the-art algorithms, as is demonstrated through a detailed experimental evaluation and verified statistically.     

Niche identification techniques in multimodal genetic search with sharing scheme

Genetic algorithms with sharing have been applied in many multimodal optimization problems with success. Traditional sharing schemes require the definition of a common sharing radius, but the predefined radius cannot fit most problems where design niches are of different sizes. Yin and Germay proposed a sharing scheme with cluster analysis methods, which can determine design clusters of different sizes. Since clusters are not necessarily coincident with niches, sharing with clustering techniques fails to provide maximum sharing effects. In this paper, a sharing scheme based on niche identification techniques (NIT) is proposed, which is capable of determining the center location and radius of each of existing niches based on fitness topographical information of designs in the population. Genetic algorithms with NIT were tested and compared to GAs with traditional sharing scheme and sharing with cluster analysis methods in four illustrative problems. Results of numerical experiments showed that the sharing scheme with NIT improved both search stability and effectiveness of locating multiple optima. The niche-based genetic algorithm and the multiple local search approach are compared in the fifth illustrative problem involving a discrete ten-variable bump function problem.     

An algorithm for the selection problem

A refinement to a well-known selection algorithm is described. The refinement results in a useful improvement in the performance of the original algorithm, particularly when the selection index is small relative to the median.     

基于接入概率的LTE小区重选优化算法分析

在3G网络中,UE(用户设备)在建立RRC(无线资源控制)连接之前会进行接入等级检查.在一些具有低接入概率的小区中,UE可能会由于低接入概率而尝试多次接入服务小区,从而导致较高的失败连接次数和更长的接入延迟.提出一种基于接入概率的小区重选优化算法对于小区重选R准则算法进行优化,进而使处于服务小区内低接入概率的UE更容易重选到高接入概率的邻小区,并通过建模和仿真分析了算法的性能;结合接入概率对小区重选中的相关参数进行了分析,提出合适的参数设置.     

Searching performance of a real-coded genetic algorithm using biased probability distribution functions and mutation

One excellent crossover method for the real-coded genetic algorithm (RGA) is the unimodal normal distribution crossover method (UNDX). The UNDX is superior to the blend crossover method (BLX). The UNDX uses Gaussian distribution functions based on the main and sub searching lines. In this article, we present a method of improving the searching performance of the RGA. We propose the use of biased probability distribution functions (BPDFs) based on the main and sub searching lines in the crossover process. The crossover with BPDFs frequently produces offspring that are close to the best individuals in the current generation, and it is highly likely that these offspring will offer the best solution to the problem. Furthermore, we propose a mutation that has a constant and extended range that is wider than that of the UNDX. Simulations show the efficiency of the proposed method. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

A new local search based hybrid genetic algorithm for feature selection

This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS), called as HGAFS. The vital aspect of this algorithm is the selection of salient feature subset within a reduced size. HGAFS incorporates a new local search operation that is devised and embedded in HGA to fine-tune the search in FS process. The local search technique works on basis of the distinct and informative nature of input features that is computed by their correlation information. The aim is to guide the search process so that the newly generated offsprings can be adjusted by the less correlated (distinct) features consisting of general and special characteristics of a given dataset. Thus, the proposed HGAFS receives the reduced redundancy of information among the selected features. On the other hand, HGAFS emphasizes on selecting a subset of salient features with reduced number using a subset size determination scheme. We have tested our HGAFS on 11 real-world classification datasets having dimensions varying from 8 to 7129. The performances of HGAFS have been compared with the results of other existing ten well-known FS algorithms. It is found that, HGAFS produces consistently better performances on selecting the subsets of salient features with resulting better classification accuracies.     

基于锦标赛选择遗传算法的随机微粒群算法

以保证全局收敛的随机微粒群算法SPSO为基础。提出了一种改进的随机微粒群算法-GAT-SPSO。该方法是在SPSO的进化过程中．以锦标赛选择机制下的遗传算法所产生的最优个体来代替SPSO中停止的微粒,参与下一代的群体进化。通过时三个多峰的测试函数进行仿真,其结果表明：在搜索空间维数相同的情况下,GAT-SPSO的收敛率厦收敛速度均大大优于SPSO。     

基于遗传算法的云服务商伙伴选择问题研究

针对目前云计算市场如何选择合适的云服务商来组成动态联盟,以便更快更有效地满足终端客户的需求,实现云服务资源的优化配置.运用灰色关联综合评价模型确定云服务市场的优化指标,运用多目标优化模型定量分析和研究了云服务商的伙伴选择问题,选取在云计算市场提供计算服务、存储服务、软件服务的云服务商作为研究对象,提取成本、响应时间、服务质量作为研究优化指标;通过赋予相应的权重值,采用遗传算法对多目标规划化问题进行求解,寻找到符合各个云服务商利益的合作伙伴,最后通过算例证明该算法在解决最佳云服务商伙伴选择组合方面的合理性,验证了该模型及算法的有效性.     

A niched genetic algorithm to solve a pollutant emission reduction problem in the manufacturing industry: A case study

A multiobjective optimization approach to deal with a pollutant emission reduction problem in the manufacturing industry, through implementation of the best available technical options, is presented in this paper. More specifically, attention is focused on the industrial painting of wood and the problem under investigation is formulated as a bicriteria combinatorial optimization problem. A niched Pareto genetic algorithm based approach is used to determine sets of methods, tools and technologies, applicable both in the design and in the production phase, allowing to simultaneously minimize the total cost and maximize the total pollutant emission reduction.     

A hierarchical genetic algorithm with age structure for multimodal optimal design of hybrid composites

A genetic algorithm aiming the optimal design of composite structures under non-linear behaviour is presented. The approach addresses the optimal material/stacking sequence in laminate construction and material distribution topology in composite structures as a multimodal optimization problem. The proposed evolutionary process is based on a sequential hierarchical relation between subpopulations evolving in separated isolation stages followed by migration. Improvements based on the species conservation paradigm are performed to avoid genetic tendencies due to elitist strategies used in the hierarchical subpopulations. The concept of species is associated with material distribution topology in composite structures, and an enlarged master population with age structure is considered concurrently with the hierarchical topology. Rules based on species concept are imposed on either isolation or migration stages to overcome the predominance of a species and to guarantee the diversity. A mutation process controlled by the stress field is implemented, improving the local genetic search. The proposed model allows multiple solutions for the optimal design problem.