多目标进化算法中多样性策略的研究

进化多目标优化中由于进化算子固有的随机误差以及进化过程中选择压力和选择噪音的影响使得进化群体容易丧失多样性,而保持进化群体的多样性不仅有利于进化群体搜索,而且也是多目标优化的重要目标。对多目标进化算法的多样性策略进行了分类,在统一的框架下描述了各种策略的机制,并分析了各自的特性。随后,分析并比较了多样性保持算子的复杂度。最后,证明了一般意义下多目标进化算法的收敛性,指出在设计新的多样性策略中需要保证进化世代间的单调性,避免出现退化现象。     

Pareto最优概念的多目标进化算法综述

群体搜索策略和群体间个体之间的信息交换是进化算法在解决多目标优化问题上的两大优势.目前,基于Pareto最优概念的多目标进化算法已成为多目标优化问题研究的主流方向.详细介绍了该领域的经典算法,特别对各种算法在种群快速收敛并均匀分布于问题的非劣最优域上所采取的策略进行了阐述,并归纳了算法性能评估中需要深入研究的问题.     

量子多目标进化算法研究

首次将量子计算的理论用于多目标优化,提出量子多目标进化算法(QMOEA),其采用量子位染色体表示法,利用量子门旋转策略和量子变异实现群体的进化,使用!支配关系构造外部种群以此保持算法的较好分布性,提出基于快速排序的非劣最优解构造方法加快算法运行效率,实验表明,这种方法与经典的多目标进化算法SPEA2相比,其收敛性更好且分布更均匀。     

多目标优化的进化环境模型及实现

传统多目标进化算法主要是模仿生物自身的进化过程,没有考虑环境对进化的作用,缺乏能动的、指导性的搜索.提出一种基于进化环境的多目标进化模型,利用进化环境记录群体进化过程中产生的知识信息,并反过来指导群体搜索,实现环境与群体的共同进化.此外,给出基于进化环境的多目标进化模型的一种算法实现,利用环境域和单元域表示进化环境,设置了一组环境规则,从而实现进化环境对进化群体的约束、促进和导向作用.通过与5个代表性经典多目标进化算法,对12个具有不同特征和不同求解难度的测试函数,在Generational Distance、Hypervolume和Inverted Generational Distance三项性能指标上进行比较实验,验证了文中所提出的算法具有良好的收敛性和综合性能.     

基于免疫原理的多目标进化算法群体多样性研究

在多目标进化系统中一个关键问题是必须采取某一措施来保持群体多样性.本文将自然生命的免疫机制引入多目标进化过程,提出一种基于免疫原理的多目标进化算法群体多样性保持策略,并给出了具体设计方法.最后以两个目标的流水车间作业调度问题为例进行计算机模拟求解,将本文方法与"多目标遗传算法"的计算结果进行了对比,实验表明该方法能有效保持群体多样性,多目标优化结果的性能较好.     

一种保持群体多样性的多目标遗传算法

提出一种保持群体多样性的多目标遗传算法.该算法采用一种基于信息熵的测度,以衡量群体在多日标空间下的多样性,并利用该测度将群体当前的进化状态(多样性)与算法的运行机制相关联,设计了若干种增强算法探索力度的策略,有效地开拓了算法的搜索范围,提高了进化过程中群体的多样性,防止了算法早熟收敛.对所提出算法的计算复杂度进行了理论分析.仿真实验表明,所提出的算法具有较好的收敛性能和分布特性.     

一种子群体个数动态变化的多目标优化协同进化算法

给出一种新型的在多目标优化条件下的进化算法群体停滞判别准则，并基于该准则提出一种合作型多目标优化协同进化算法．该算法在运行过程中自适应地决定子群体的新增和灭绝．使得子群体个数依据需要动态变化，减小了对计算资源的消耗，并解决了对复杂多目标优化问题难以事先进行分解的问题．对所提算法的计算复杂度进行了理论分析，并把它与已有的多目标进化算法进行了比较，结果表明所提算法具有较高的搜索性能．     

一个用于多目标优化的进化规划算法

进化计算的群体搜索机制为多目标优化问题的直接求解提供了途径.本文将多目标遗传算法中的一些技术用于进化规划,提出一个多目标进化规划算法,并给出计算实例.     

一个用于多目标优化的进化规划算法

进化计算的群体搜索机制为多目标优化问题的直接求解提供了途径。本文将多目标遗传算法中的一些技术用于进化规划,提出一个多目标进化规划算法,并给出计算实例。     

基于分解和差分进化的多目标粒子群优化算法

为了提高多目标优化算法解集的分布性和收敛性,提出一种基于分解和差分进化的多目标粒子群优化算法(dMOPSO-DE).该算法通过提出方向角产生一组均匀的方向向量,确保粒子分布的均匀性;引入隐式精英保持策略和差分进化修正机制选择全局最优粒子,避免种群陷入局部最优Pareto前沿;采用粒子重置策略保证群体的多样性.与非支配排序(NSGA-II)算法、多目标粒子群优化(MOPSO)算法、分解多目标粒子群优化(dMOPSO)算法和分解多目标进化-差分进化(MOEA/D-DE)算法进行比较,实验结果表明,所提出算法在求解多目标优化问题时具有良好的收敛性和多样性.     

The effect of dispersal on population growth with stage-structure

Declines in species richness or population are primarily attributed to habitat destruction and fragmentation. Can we avoid the local extinction of species with stage-structure in some patches by building some corridors between the patches and controlling the dispersal rates? A conservation strategy is put forward by introducing and analyzing the asymptotic behavior of some autonomous and time-varying population models. Biological implications of these results are discussed briefly.     

多目标优化差分进化算法

个体的适应度赋值和群体的多样性维护是进化算法的两个关键问题。首先,一方面,定义了Paretoε-支配关系的相关概念,通过Paretoε-支配关系确定个体的强度Pareto值,根据个体的强度Pareto值对群体进行Pareto分级排序,实现优胜劣汰;另一方面,使用拥挤距离估算个体的拥挤密度,淘汰位于拥挤区的一些个体,维持群体的多样性。然后,根据差分进化算法的特点,使用适当的进化策略和控制参数,给出了一种用于求解多目标优化问题的差分进化算法DEAMO。最后,数值实验表明,DEAMO在求解标准的多目标优化问题时性能表现优良。     

一种具有动态群体规模的微粒群算法

以标准微粒群算法PSO为基础,提出了一种改进的群体规模可变的微粒群算法—VPPSO。该方法是在标准PSO的进化过程中,当PGBEST(全局最好值)连续多代不发生变化时,利用遗传算法的杂交机制产生子代,并根据一定的规则加入进化群体中,当群体规模超过允许的最大值时,再通过选择机制,将群体规模收缩到初始时的状态。通过对四个多峰测试函数进行仿真,其结果表明:在高维多峰函数的优化中,VPPSO的收敛率以及收敛精度较标准PSO有很大的提高。     

简便的遗传算法在SSFCTP中的应用

对SSFCTP进行了研究,给出了一种简便的遗传算法,这种算法是在基本遗传算法的基础上省略掉杂交过程,仅仅通过对种群中每一个个体的基因进行随机重组而生成新种群.这样一来既可以减少计算量,提高计算效率,也可以保证种群中产生个体的多样性和可行性.并用实例验证和比较了它与基本遗传算法在解决此类问题中的有效性.同时也根据问题解的特殊结构,给出并证明了最优解的一些性质.     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Improved RM-MEDA with local learning

In this paper, local learning is proposed to improve the speed and the accuracy of convergence performance of regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), a typical multi-objective optimization algorithm via estimation of distribution. RM-MEDA employs a model-based method to generate new solutions, however, this method is easy to generate poor solutions when the population has no obvious regularity. To overcome this drawback, our proposed method add a new solution generation strategy, local learning, to the original RM-MEDA. Local learning produces solutions by sampling some solutions from the neighborhood of elitist solutions in the parent population. As it is easy to search some promising solutions in the neighborhood of an elitist solution, local learning can get some useful solutions which help the population attain a fast and accurate convergence. The experimental results on a set of test instances with variable linkages show that the implement of local learning can accelerate convergence speed and add a more accurate convergence to the Pareto optimal.     

Seeing white: Qualia in the context of decoding population codes.

When the nervous system is presented with multiple simultaneous inputs of some variable, such as wavelength or disparity, they can be combined to give rise to qualitatively new percepts that cannot be produced by any single input value. For example, there is no single wavelength that appears white. Many models of decoding neural population codes have problems handling multiple inputs, either attempting to extract a single value of the input parameter or, in some cases, registering the presence of multiple inputs without synthesizing them into something new. These examples raise a more general issue regarding the interpretation of population codes. We propose that population decoding involves not the extraction of specific values of the physical inputs, but rather a transformation from the input space to some abstract representational space that is not simply related to physical parameters. As a specific example, a four-layer network is presented that implements a transformation from wavelength to a high-level hue-saturation color space.     

An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry

Teaching-learning-based optimization (TLBO) algorithm is a novel nature-inspired algorithm that mimics the teaching and learning process. In this paper, an improved version of TLBO algorithm (I-TLBO) is investigated to enhance the performance of original TLBO by achieving a balance between exploitation and exploration ability. Inspired by the concept of historical population, two new phases, namely self-feedback learning phase as well as mutation and crossover phase, are introduced in I-TLBO algorithm. In self-feedback learning phase, a learner can improve his result based on the historical experience if his present state is better than the historical state. In mutation and crossover phase, the learners update their positions with probability based on the new population obtained by the crossover and mutation operations between present population and historical population. The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO. The effectiveness of proposed I-TLBO algorithm is tested on some benchmark functions and a combinatorial optimization problem of heat treating in foundry industry. The comparative results with some other improved TLBO algorithms and classic algorithms show that I-TLBO algorithm has significant advantages due to the balance between exploitation and exploration ability.     

Bidirectional evolutionary heuristic for the minimum vertex-cover problem

In this study, we have solved the minimum vertex-cover problem, NP-hard, by the means of genetic algorithms (GA). In prior studies, initial population on which GA iteratively run, was created randomly. However, we have improved this technique to create initial population and the population created has some useful properties such as decreasing the number of iterations of GA algorithm and gets rid of diversity problem, local solution problem. Initially, one chromosome is created and then the inversion of this chromosome is taken as another chromosome. If it is required to create an initial population with large set of chromosomes, then randomly created chromosomes can be divided into desired partitions and other chromosomes can be obtained by the complement of each partition at a time. Search is handled in bi-directional manner, and the initial population contains both ends of solution space.     

基于种群多样性的可变种群缩减差分进化算法

为了更有效地避免早熟收敛,提高算法的全局搜索能力,提出了基于种群多样性的可变种群缩减差分进化算法(Dapr-DE)。首先,Dapr-DE使用群体多样性指标控制种群规模缩减;然后,使用聚类将种群分为不同类簇,在类簇中根据适应度值删除个体,既维持了种群的多样性,又减少了由于 存在过多相似个体而导致的局部收敛。最后在CEC14测试集的30个函数优化问题上进行了实验比较,验证了所提算法的有效性。