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.     

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.     

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。     

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。     

用适应值激励机制提高遗传算法的效率

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。     

基于离散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不确定性的问题,采用离散适应值评价进化个体,利用灰度衡量评价的不确定性。通过确定离散适应值的灰度,获得反映种群进化分布的信息;基于此,给出了进化个体的自适应交叉和变异概率。将该算法应用于服装进化设计系统,仿真实例与分析结果表明,所提出的算法可以有效缓解人的疲劳,提高优化效率。     

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。     

基于个体适应值灰模型的交互式遗传算法

为将交互式遗传算法应用于复杂的优化问题中,提出一种基于进化个体适应值灰模型预测的交互式遗传算法,为每代适应值序列建立灰模型,以衡量个体适应值评价的不确定性,通过对灰模型的灰预测,提取进化个体评价的可信度,在此基础上,给出进化个体适应值修正公式,将该算法应用于服装进化设计系统中。实验结果表明,该算法在每代都能获取更多的满意解。     

面向多目标优化的适应度共享免疫克隆算法

多目标优化的日标在于使得解集能够快速的逼近真实Pareto前沿.针对解的分布性问题,以免疫克隆算法为框架,引入适应度共享策略,提出了一种新的具有良好分布性保持的多目标优化进化算法;算法建立外部群体以保存非支配解,以Pareto优和共亨适应度作为外部群体更新与激活抗体选择的双重标准.为了增强算法对决策空间的开发能力,引入...     

基于VNF实例共享的服务功能链部署算法

针对服务功能链(SFC)部署过程中存在虚拟网络功能(VNF)实例部署成本和转发路径成本难以权衡的问题,提出了基于VNF实例共享的SFC部署算法。首先针对多链SFC建立VNF和虚拟链路映射模型,并预估路径部署长度上限,保证SFC时延需求;其次,在路径部署长度限制范围内,尽可能使VNF实例共享最大化,以平衡链路转发成本和VNF部署成本,最终得到SFC部署策略。与已有的SPH(shortest path heuristic)和GUS(greedy on used server)部署算法相比,所提算法所得的总运营成本分别降低6.6%和12.15%,且当SFC数量增多时,该算法的服务接受率可达89.33%。仿真实验结果表明,提出算法可以在保证用户服务质量的同时有效降低SFC部署成本。     

面向多模态函数优化的回溯克隆选择算法

针对多模态函数优化问题,提出了一种基于回溯机制的改进克隆选择算法--回溯克隆选择算法（BCSA）,采用改进回溯机制和记忆库抗体抑制策略,保持了抗体的多样性,以增强算法的全局搜索能力;通过改进动态变异、选择与交叉操作提高算法收敛速度。典型的多模态函数测试结果表明：回溯克隆选择算法具有优良的全局搜索能力和搜索效率。     

Fuzzy genetic sharing for dynamic optimization

Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).     

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.     

基于误码率的快速中继选择算法

针对协作网络中的中继选择算法的最优性与运算效率的矛盾问题,在放大转发(AF)协作网络中,提出一种基于误码率的快速中继选择算法。该算法先在等功率条件下,根据信道统计特性及系统误码率,引入一个等效信道增益参数,该参数反映了在协作通信过程中,源节点到中继节点以及中继节点到目的节点两个阶段的信道特性。然后将该参数降序排列,以当前信噪比(SNR)为门限,在等功率条件下选择中继节点集合,使系统的误码率最小。并结合次优功率分配,进一步降低系统的误码率。仿真结果表明,该算法能够取得和穷举算法相似的性能,但计算复杂度至少降低到穷举算法的1/20,且随着中继节点数的增加计算复杂度更进一步降低。同时,仿真结果还表明该中继选择算法的误码率性能优于所有中继节点参与转发（AP-AF）及预先选择一个最优中继节点转发数据（S-AF）算法。     

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 exact constructive algorithm for the knapsack sharing problem

In this paper, we study the knapsack sharing problem (KSP), a variant of the well-known NP-hard single knapsack problem. We propose an exact constructive tree search that combines two complementary procedures: a reduction interval search and a branch and bound. The reduction search has three phases. The first phase applies a polynomial reduction strategy that decomposes the problem into a series of knapsack problems. The second phase is a size reduction strategy that makes the resolution more efficient. The third phase is an interval reduction search that identifies a set of optimal capacities characterizing the knapsack problems. Experimental results provide computational evidence of the better performance of the proposed exact algorithm in comparison to KSPs best exact algorithm, to Cplex and to KSPs latest heuristic approach. Furthermore, they emphasize the importance of the reduction strategies.     

基于交叉迁移和共享调整的改进蝴蝶优化算法

针对蝴蝶优化（monarch butterfly optimization,MBO）算法易陷入局部最优和收敛速度慢等问题,提出了一种基于改进的交叉迁移和共享调整的蝴蝶优化（MBO with cross migration and sharing adjustment,CSMBO）算法。首先,利用基于维度的垂直交叉操作来替换标准MBO算法的迁移算子,形成交叉迁移算子,有效提升其搜索能力;其次,将原始调整算子改为具有信息分享功能的共享调整算子,以加快算法的收敛速度;最后,采用贪婪选择策略取代标准MBO算法中的精英保留策略,减少一次排序操作进而提高其计算效率。为了验证CSMBO算法的优化能力,测试了其在30维和50维函数上的优化,并与三种优化算法进行比较,其实验结果表明CSMBO算法具有良好的优化性能。