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粒子群优化与差分进化混合算法的综述与分类 总被引:2,自引:0,他引:2
优化算法的性能改进长期以来一直是算法研究者们追求的一个重要目标,对不同算法进行混合以期利用算法的互补优势来获得性能更优异的算法代表了一类典型的设计思想.针对两类基于群体演化的优化算法——粒子群优化(PSO)与差分进化(DE)算法,对基于二者的各种混合算法(DEPSO)进行了系统而全面的综述,并在此基础上提出了一种混合策... 相似文献
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《数学的实践与认识》2018,(22)
为了更加有效解决多目标优化问题,提出了一种基于差分变异和扰动变异相结合的多目标克隆选择算法(MCSA-MDD).该算法对非支配抗体进行克隆操作,从而有利于向着理想Pareto前端搜索;采用差分变异与扰动变异相结合的方式来进行免疫基因操作,有利于解的多样性.数值实验分为两组,一组选取4个常用测试函数并与其它五个多目标算法进行比较,数值实验结果表明了MCSA-MDD算法的有效性.另一组仅用差分变异和扰动变异的多目标免疫克隆算法进行比较,数值实验结果验证了采用差分变异与扰动变异相结合的免疫操作提高了算法的性能. 相似文献
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C*-代数的*-同构一定是(完全)等距映射,反之不然.本文证明了C*-代数的实完全等距映射能够完全决定C*-代数*-同构的结论. 相似文献
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刻画了无限维实或复Banach空间上的标准算子代数间完全保对合性的可加映射,证明了这样的映射是同构的常数倍或(复情形下)共轭同构的常数倍. 相似文献
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针对极限学习机的随机性较大的问题,提出一种基于差分演化的极限学习机算法模型(DE-ELM).采用差分演化算法(DE)对极限学习机(ELM)随机给定的输入权值矩阵和隐含层阈值进行寻优,降低了随机性给ELM造成的影响,减少ELM网络震荡,提高了ELM预测精度.并且将DE-ELM应用在电池SOC的预测上,同时与ELM和BP神经网络的预测进行了对比,结果表明:DE-ELM在电池SOC预测上的表现优于ELM和BP神经网络,能满足电池SOC的预测精度要求. 相似文献
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基于改进遗传算法的布局优化子问题 总被引:2,自引:0,他引:2
本针对子问题,构造了布局子问题(关于同构布局等价类)的改进遗传算法。将该算法应用于二维布局优化子问题,数值实验表明该算法能够在很好地保持图元的邻接关系的前提下找到子问题的最优解。由于布局优化问题可分解为有限个子问题,所以利用该算法可以找到整个布局优化问题的全局最优解。 相似文献
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《数学的实践与认识》2015,(22)
使用径向基函数(Radial Basis Function,RBF)神经网络为桥式起重机设计一种防摇摆控制器,并采用遗传算法(Genetic Algorithm,GA)与粒子群优化算法(Particle Swarm Optimization,PSO)相结合的混合进化算法(Hybrid Evolutionary Algorithm,HEA)作为神经网络的学习算法.RBF神经网络用于生成台车运动的光滑轨迹,而混合进化算法以台车遵循所生成轨迹到达目标位置时起重机系统的机械能为优化目标,对神经网络的参数进行优化调整,从而达到抑制负载残余摆动的目的.最后通过仿真验证了所提出的混合进化算法相对于遗传算法和粒子群优化算法的优越性以及所设计的防摇摆控制器的正确性和有效性. 相似文献
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作业车间调度是一类求解困难的组合优化问题,本文在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法相结合,提出了一种基于遗传和禁忌搜索的混合算法,并用实例对该算法进行了仿真研究.结果表明,该算法有很好的收敛精度,是可行的,与传统的算法相比较,有明显的优越性. 相似文献
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《International Journal of Approximate Reasoning》2006,41(1):59-73
Genetic algorithms (GAs) pose several problems. Probably, the most important one is that the search ability of ordinary GAs is not always optimal in the early and final stages of the search because of fixed GA parameters. To solve this problem, we proposed the fuzzy adaptive search method for genetic algorithms (FASGA) that is able to tune the genetic parameters according to the search stage by the fuzzy reasoning. In this paper, a fuzzy adaptive search method for parallel genetic algorithms (FASPGA) is proposed, in which the high-speed search ability of fuzzy adaptive tuning by FASGA is combined with the high-quality solution finding capacity of parallel genetic algorithms. The proposed method offers improved search performance, and produces high-quality solutions. Moreover, we also propose FASPGA with an operation of combining dynamically sub-populations (C-FASPGA) which combines two elite islands in the final stage of the evolution to find a better solution as early as possible. Simulations are performed to confirm the efficiency of the proposed method, which is shown to be superior to both ordinary and parallel genetic algorithms. 相似文献
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该文给出了一种用于多处理机系统中实现并行计算的最优映射问题的遗传算法,它对于在固定结构的并行系统中充分利用计算资源,提高计算效率具有实用价值,实践表明,采用遗传算法是解决任务最优映射问题的有效的方法. 相似文献
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Vicente Valls Francisco Ballestín Sacramento Quintanilla 《European Journal of Operational Research》2008
In this paper we propose a Hybrid Genetic Algorithm (HGA) for the Resource-Constrained Project Scheduling Problem (RCPSP). HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule found in the first phase. The computational results show that HGA is a fast and high quality algorithm that outperforms all state-of-the-art algorithms for the RCPSP known by the authors of this paper for the instance sets j60 and j120. And that it is competitive with other state-of-the-art heuristics for the instance set j30. 相似文献
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二维恒定各向同性介质渗透系数反演的遗传算法 总被引:1,自引:0,他引:1
给出了利用遗传算法求解二维恒定各项同性介质渗透系数反演的一种新方法,该方法把参数反演问题转化为优化问题通过遗传算法求解.数值模拟结果表明:该方法具有精度高、收敛速度快、编程简单、易于计算机实现等优点,值得在实际工作采用. 相似文献
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以往关于信任的研究是在稳定均衡的假设下进行的,然而信任演化过程中会表现出非线性的混沌状态,具有复杂系统的特征。基于演化博弈理论和混沌理论,建立了创新网络中组织间信任演化模型,分析了创新网络中组织间信任的复杂性、初值敏感性、分岔行为及内随机性等混沌特性,推导出信任演化方程与Logistic映射之间的关系,采用Lyapunov稳定性理论进行混沌性判定,证明创新网络中组织间信任通过倍周期分岔通往混沌,得到了信任从有序进入混沌的一般条件,运用算例进行仿真展示信任演化通往混沌的过程,分析创新网络中信任演化进入混沌区的实际意义,并选择硅谷和筑波科技城两个实例做对比分析,验证了该研究的实用性和有效性。创新网络中组织间信任的混沌演化反映出信任发展的非线性特点,为创新网络中组织间信任的混沌利用和控制提供理论指导。 相似文献
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Models for the genetic evolution of natural populations have supplied the inspiration for the adaptive computer algorithms known as “genetic algorithms.” In its original form, a genetic algorithm simulates the evolution of a haploid population: Genetic variation is produced by mutation at a number of genetic loci in a chromosome, represented as a string of bits, and the variants in the various chromosomes are reshuffled by genetic recombination. In nature, the ability of a haploid individual to survive and reproduce is expressed as its fitness; in computer science the value of a string is measured by its ability to program a given task. The performance of genetic algorithms is evaluated in the “schemata theorem,” which we present and discuss in the context of the population genetics of multiple loci and propose a generalization of the theorem. © 1998 John Wiley & Sons, Inc. 相似文献
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The Internet has ossified. It has lost its capability to adapt as requirements change. A promising technique to solve this problem is the introduction of network virtualization. Instead of directly using a single physical network, working just well enough for a limited range of applications, multiple virtual networks are embedded on demand into the physical network, each of them perfectly adapted to a specific application class. The challenge lies in mapping the different virtual networks with all the resources they require into the available physical network, which is the core of the virtual network mapping problem. In this work, we introduce a memetic algorithm that significantly outperforms the previously best algorithms for this problem. We also offer an analysis of the influence of different problem representations and in particular the implementation of a uniform crossover for the grouping genetic algorithm that may also be interesting outside of the virtual network mapping domain. Furthermore, we study the influence of different hybridization techniques and the behaviour of the developed algorithm in an online setting. 相似文献
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本文研究了遗传算法易发生"早熟"以及人工蜂群算法在搜索初期寻优速度慢的问题.基于将遗传算法与人工蜂群算法融合以实现二者互补的思想,提出遗传-人工蜂群融合算法(G-ABCA),利用马尔可夫理论对其收敛性进行了理论分析,证明其适应度函数值序列(即优化解满意值序列)是单调且收敛的,并利用四个经典的多峰测试函数对遗传-人工蜂群融合算法、改进的遗传算法以及人工蜂群算法进行了对比实验分析,结果表明:遗传-人工蜂群融合算法不仅收敛,而且其寻优性能显著优于其它两种算法. 相似文献