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。     

A Review of Niching Genetic Algorithms for Multimodal Function Optimization

In this paper, a comprehensive review of approaches to solve multimodal function optimization problems via genetic niching algorithms is provided. These algorithms are presented according to their space–time classification. Methods based on fitness sharing and crowding methods are described in detail as they are the most frequently used.     

基于NPSO算法求解车间作业调度问题

研究车间生产生产调试系统,使资源达到优化配置,实现了一种基于小生境的粒子群优化算法用于求解车间作业调度问题。通过在粒子群算法中引入共享函数和共享适应度函数分别用来计算粒子间的共享度和粒子的共享适应值,并用粒子的共享适应值来反映其适应能力。粒子的位置越相近,则粒子间的共享度越大,相应粒子的共享适应值则越小。通过设置小生境半径的方式,将整个粒子群分解为多个小生境子种群,并通过设置小生境中的最大粒子个数参数,严格控制各个小生境中的粒子数量,使得所有粒子尽可能地分布到整个搜索空间的不同局部峰值区域,从而有效求得问题的全局最优值。仿真结果表明了算法对经典JSP问题求解的优良性能。     

基于（μ＋λ）选择策略的多目标优化分段遗传算法

在多目标优化遗传算法中，将整个种群按目标函数值划分成若干子种群，在各子种群内μ个父代经遗传操作产生λ个后代；然后将各子种群的所有父代和后代个体收集起来进行种群排序适应度共享，选取较好的个体组成下一代种群。相邻的非劣解容易分在同一子种群有利于提高搜索效率；各子种群间的遗传操作可采用并行处理；各子种群的所有
有个体收集起来进行适应度共享有利于维持种群的多样性。最后给出了计算实例。     

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...     

适应值共享对遗传算法选择概率的影响分析

研究了采用遗传算法进行多蜂函数优化时引入适应值共享机制对选择概率的影响。引入种群共享因子这一参数，描述个体选择概率、小生境中多个个体的选择概率之和在适应值比例选择策略下的变化情况。分析和仿真实验表明，适应值共享可在一定程度上保持种群多样性，适应值函数的取值范围将对优化结果产生较大的影响。     

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.     

免疫调节优化算法及其对控制系统的参数估计

借鉴免疫系统中T细胞调节的思想、抗体重复变异的亲和成熟特征、小生境共享适应度方法及相关的免疫机制,提出一种适合于函数优化的新型智能优化算法。算法设计的关键在于利用T细胞调节机制动态调节参与变异的抗体数日,以及利用抗体亲和成熟特征设计自适应突变的算子模块;突出的优点为自适应能力强、寻优速度快、多途径产生进化群体及易于与相关算法融合。事例仿真效果论证了其可行性和有效性。     

Efficient and scalable Pareto optimization by evolutionary local selection algorithms

Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.     

求解VLSI布图规划问题的多目标粒子群优化算法

布图规划在超大规模集成电路(VLSI)物理设计过程中具有重要作用,它是一个多目标组合优化问题且被证明是一个NP问题。为了有效解决布图规划问题,本文提出一个多目标粒子群优化(PSO)算法。该算法采用序列对表示法对粒子进行编码,根据遗传算法交叉算子的思想对粒子更新公式进行了修改;引入Pareto最优解的概念和精英保留策略,并设计了一个基于表现型共享的适应值函数以维护种群的多样性。仿真实验通过对MCNC标准问题的测试表明了本文算法是可行且有效的。     

多模态函数优化的多种群进化策略

在一种使用单基因变异、精英繁殖、递减型策略参数的改进进化策略基础上,提出了一种求解多模态函数多个极值点的多种群协同进化策略,并给出了子种群进化概率、停止条件的确定和收敛到极值点的判断条件,在求多极值点的进化算法中,判别两个极值点是同峰还是异峰极值点是一个困难而关键的问题,为此引入了一种新的判别方法——山谷探索法,从而避免了确定小生境单径或峰半径,一组测试函数的仿真计算结果表明了所提出的算法能准确地找到全部极值点.     

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.     

Varying fitness functions in genetic algorithm constrainedoptimization: the cutting stock and unit commitment problems

We present a specific varying fitness function technique in genetic algorithm (GA) constrained optimization. This technique incorporates the problem's constraints into the fitness function in a dynamic way. It consists of forming a fitness function with varying penalty terms. The resulting varying fitness function facilitates the GA search. The performance of the technique is tested on two optimization problems: the cutting stock, and the unit commitment problems. Also, new domain-specific operators are introduced. Solutions obtained by means of the varying and the conventional (nonvarying) fitness function techniques are compared. The results show the superiority of the proposed technique.     

一种基于混沌遗传与粒子群混合优化的过程神经网络训练算法

针对时变输入/输出过程神经网络的训练问题,提出一种基于混沌遗传与带有动态惯性因子的粒子群优化相结合的学习方法。综合利用粒子群算法的经验记忆、信息共享和混沌遗传算法的混沌轨道遍历搜索性质,基于PNN训练目标函数,构建两种算法相混合的进化寻优机制,通过适应度评估和优化效率分析自适应调节混沌遗传与粒子群算法的切换,实现网络参数在可行解空间的全局优化求解。实验结果表明,该算法较大提高了PNN的训练效率。     

基于粒子群优化算法的室内定位方法

为了减小无线传感器网络节点的定位误差,将粒子群优化算法应用于定位中,与以往的适应度函数来源不同,为解决误差累积问题,在最小二乘原理基础上采用加权系数,确定适应度函数的表达式。在粒子群优化算法中引入三种不同的参数组合观察不同的参数对迭代次数以及定位精度的影响,然后通过两种不同的适应度函数对定位误差进行比较。实验结果表明,合适的参数选择能降低算法的复杂度,新的适应度函数更能减小定位误差。     

一种多目标粒子群改进算法的研究

针对多目标粒子群优化过程中的粒子飞行偏向性和多样性损失问题,提出一种基于最大最小适应函数的改进算法．该算法在最大最小适应函数的计算中引入了函数相对值算法和ε-支配的概念,并提出了变ε-支配的策略,改进了最大最小适应函数的计算方法,解决了粒子飞行过程中的偏向性和多样性损失问题,加快了算法的收敛速度．将该改进算法应用于直流变频压缩机启动时峰值电流和启动转速的优化问题,应用结果表明该算法收敛速度快且效果良好．     

A systematic approach to determining metamodel scope for risk-based optimization and its application to water distribution system design

Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.     

Dynamics of fitness sharing evolutionary algorithms for coevolution of multiple species

This paper builds the normal model of fitness sharing with proportionate selection on real-valued functions, and derives the dynamic formula to describe the evolution process of the population with the fitness sharing. The normal modeling simulation is investigated on specific test functions, and experimental results illustrate that the normal model is able to describe exactly the dynamics of the fitness sharing EAs and is a good platform to study the behavior of the fitness sharing EAs with regard to niching radius. The experimental results of the normal modeling simulation and the fitness sharing EAs verify the dilemma in finding optimal niche radius to achieve both good niching convergence and niching efficiency, for which a hybrid scheme is proposed to carry out the niching task.     

Weighted local sharing and local clearing for multimodal optimisation

Local sharing is a method designed for efficient multimodal optimisation that combines fitness sharing with spatially structured populations and elitist replacement. In local sharing, the bias towards sharing and the influence of spatial structure is controlled by the deme (neighbourhood) size. This introduces an undesirable trade-off; to maximise the sharing effect large deme sizes must be used, but the opposite must be true if one wishes to maximise the influence of spatial population structure. This paper introduces two modifications to the local sharing method. The first alters local sharing so that parent selection and fitness sharing operate at two different spatial levels; parent selection is performed within small demes, while the effect of fitness sharing is weighted according to the distance between individuals in the entire population structure. The second method replaces fitness sharing within demes with clearing to produce a method that we call local clearing. The proposed methods, as tested on several benchmark problems, demonstrate a level of efficiency that surpasses that of traditional fitness sharing and standard local sharing. Additionally, they offer a level of parameter robustness that surpasses other elitist niching methods, such as clearing. Through analysis of the local clearing method, we show that this parameter robustness is a result of the isolated nature of the demes in a spatially structured population being able to independently concentrate on subsets of the desired optima in a fitness landscape.