一种新颖的全局寻优算法—启发式进化规划

51．引言在电力系统和其它工程技术领域中，有很多含有复杂的目标函数或约束条件的优化问题，这些问题中有的是高度非线性的．而用于解决此类问题的常规的基于梯度寻化技术的各种算法，计算速度虽快，但要求优化问题可微．通常只能求得局部最优解或接近最优解时难以收敛．为此，在本世纪六十年代中期，一些科学家研究发展起来了一种用于模仿生物和人类进化来求解复杂优化问题的方法一模拟进化优化方法【‘－‘］（OntimizationmethodbySimulat。dE、lution）．该方法对目标函数或约束条件，既不要求连续，又不要求可微，只要问题是可计算…     

解约束优化问题的进化策略与混合进化策略的比较

卜．引言模拟进化计算是近年来信息科学，计算机科学的“热点”研究领域，由此派生的求解代化问题的进化策略是一种崭新的优化方法．它的基本思想来源于六十年代末Rechenberg山模拟生物进化提出的一种随机算法．H．－P．Schwefel在问中系统地推广了山的原始策略，建立了进化策略（ESI最近该方法的研究从理论到应用都取得了一些成果，显示了非常广泛的应用前景．对非线性约束优化问题有效算法的设计是一件吸引人的工作．最近Hamaifar等人间研究了用遗传算法求解约束优化问题，得到一些结果，但存在选罚因子难和收敛速度慢等问题．针对上…     

遗传算法概述

本世纪50年代中期创立了仿生学,人们从生物进化的机理中受到启发,提出了许多用于解决复杂优化问题的新方法,如遗传算法、蚁群算法、进化规划、进化策略等。研究成果已经显示出这些算法在求解复杂优化问题（特别是离散优化问题）方面的具有很强的优越性。本文将对遗传算法做详细的介绍。     

扦值法在电机CAD中的应用

一、前言世界各国开展电机的计算机辅助设计已有三十多年的历史,五十代初用模拟计算机仿真异步电机的等值电路进行性能计算,或模拟等值热路进行温升计算。1953年美国 Veinottc·G开始用数字计算机辅助异步电机设计。从六十年代开始,国外进入普遍的计算机辅助电机设计阶段,不但加快了设计速度,而且大大提高了设计质量。六十年代末开始发展优化设计。例如1968年苏联设计同步发电机时,以最小重量为目标,通过计算机设计,在条件相同的情     

基于计算智能中遗传算法的特点

计算智能是以计算模型、数学模型为基础．以分布并行计算为特征的模拟人的智能求解问题的理论与方法。遗传算法是模拟进化算法中具有普遍影响的算法之一。文章通过对遗传算法基本原理的阐述,对其算法在应用中最关键的串的编码方式、适应函数的确定、遗传算法自身参数设定这三个问题的分析,为遗传算法在网络学习、网络设计、网络分析中的应用进行了总结归纳。     

遗传算法概要

计算机生物模拟开始于本世纪40年代,特别是近代生物理论的发展为计算机生物模拟提供了丰富的素材。早在60年代美国Michigan大学的John Holland对进化理论的一般特性产生极大兴趣,他和他的学生们把已为人们所接受的生物进化思想((1)进化过程是发生在染色体上;(2)适应性好的染色体个体比适应性差的个体有更多的繁殖机会;(3)进化发生在繁殖过程中,变异可产生不同于父代的染色体;(4)生物进化没有记忆,有关个体的信息包含在染色体结构中。)应用于机器学习中,取得成功并命名为遗传算法。     

罗马尼亚的小型、微型计算机及其应用

罗马尼亚计算机工业的发展是从六十年代中期开始的。当时,罗党和政府研究了世界技术发展的趋势,作出了在社会经济中采用计算技术的决策。制定了从1968年开始的第一个三年发展计划。在这三年中,首先在计算技术研究所引进和消化国外计算机专利。例如;中型机购买了法国“国际信息公司”专利,后来的小型机购买了美国“辛格尔”公司专利。以此为基础,先后建立了计算机厂、计算机维修公司、外围设备厂等。七十年代,罗党和政府对计算技术的发展给予了很大重视,在罗马尼亚形成了使用计算机的高潮。此     

蚁群算法概述

本世纪50年代中期创立了仿生学,人们从生物进化的机理中受到启发,提出了许多用于解决复杂优化问题的新方法,如遗传算法、蚁群算法、进化规划、进化策略等。研究成果已经显示出这些算法在求解复杂优化问题（特别是离散优化问题）方面的具有很强的优越性。本文将对蚁群算法做详细的介绍。     

计算机通信网络中容量与流量分配的优化分析

随着时代的发展和科技的进步,我国计算机技术有了突飞猛进的发展,几乎每个家庭都会拥有一至两台计算机,提高了人们生活的便捷性和工作的高效性.人们追求高品质生活的同时,更加重视计算机通信网络的性能和速度.计算机网络中的容量和流量问题可以运用遗传算法来计算.本文首先简单介绍了建立计算机网络中容量和流量分配数学模型的目的,然后探讨了应用改进的并行遗传算法进行优化的问题,最后深入分析了网络容量与流量分配优化的计算结果.     

实体模型技术及其发展

<正> 一、概述 近二十年来,计算机科学技术得到了迅速发展。无论是计算机本身还是在计算机应用方面均取得了惊人的成就。计算机辅助设计和制造(CAD/CAM)技术也从最初的简单数值计算发展到能够模拟和优化整个设计制造过程的一体化技术。而实体模型(SoIid ModeIing)是CAD/CAM领域中最活跃的一个方面。早在六十年代中期,有关实体模型理论的研究就开     

Differential evolution and quantum-inquired differential evolution for evolving Takagi–Sugeno fuzzy models

The differential evolution (DE) is a global optimization algorithm to solve numerical optimization problems. Recently the quantum-inquired differential evolution (QDE) has been proposed for binary optimization. This paper proposes DE/QDE to learn the Takagi–Sugeno (T–S) fuzzy model. DE/QDE can simultaneously optimize the structure and the parameters of the model. Moreover a new encoding scheme is given to allow DE/QDE to be easily performed. The two benchmark problems are used to validate the performance of DE/QDE. Compared to some existing methods, DE/QDE shows the competitive performance in terms of accuracy.     

Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems

Evolutionary algorithms (EAs) are general-purpose stochastic search methods that use the metaphor of evolution as the key element in the design and implementation of computer-based problems solving systems. During the past two decades, EAs have attracted much attention and wide applications in a variety of fields, especially for optimization and design. EAs offer a number of advantages: robust and reliable performance, global search capability, little or no information requirement, and others. Among various EAs, differential evolution (DE), which characterized by the different mutation operator and competition strategy from the other EAs, has shown great promise in many numerical benchmark problems and real-world optimization applications. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. To improve the global optimization property of DE, in this paper, a DE approach based on measure of population's diversity and cultural algorithm technique using normative and situational knowledge sources is proposed as alternative method to solving the economic load dispatch problems of thermal generators. The traditional and cultural DE approaches are validated for two test systems consisting of 13 and 40 thermal generators whose nonsmooth fuel cost function takes into account the valve-point loading effects. Simulation results indicate that performance of the cultural DE present best results when compared with previous optimization approaches in solving economic load dispatch problems.     

基于分布估计的离散差分演化算法

差分演化(DE)是解决优化问题的非常有效的新兴智能算法,但它主要用于连续优化领域,至今尚不能象解决连续优化问题那样有效的处理组合优化问题.首先提出了离散DE用于组合优化问题,然后在离散DE中引入分布估计算法(EDA)来提高性能,把EDA抽样得到的全局统计信息和离散DE获得的局部演化信息相结合来产生新解,形成基于EDA的离散DE算法.为了保持种群多样性,在提出的算法中引入了位翻转变异操作.实验结果表明,EDA能大大提高离散DE的性能.     

从局部极小到全局最优

所有控制决策问题本质上均可归结为优化问题,但大部分存在多极小,因此如何摆脱局部极小以实现全局最优一直是理论界和工程界关注的热点课题。文章总结了若干全局优化技术的机制和特点,包括模拟退火、进化计算、禁忌搜索、变邻域搜索、噪声方法、巢分区、混沌搜索、隧道方法、平滑技术、混合算法等,力求为优化研究人员了解全局优化技术和开发高效算法提供指导。     

A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction

Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching–learning-based optimization (TLBO)–differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO–DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.     

蚁群法研究进展及应用于化学化工

蚁群法是新型的群智能优化法,具有鲁棒性、分布并行机制和易融入启发式信息等特点。尤其通过释放信息素,蚂蚁间相互交流协作,实现正反馈机制,加速全局搜优,提高效率。蚁群法最初只用于离散问题。本文评述蚁群法的起源、改进和发展;重点介绍并分析了连续型蚁群法的几种处理方案和各自特点;全面总结蚁群法已应用在化学化工领域,以及对动态、带约束和多目标问题的处理方式和机制,效果良好,优于其他算法的情况。最后展望蚁群法今后的发展前景和研究方向。     

Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization

The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process. The fundamental nature of a search algorithm will essentially determine its search efficiency and thus the types of problems it can solve. Modern metaheuristic algorithms are generally more suitable for global optimization. This paper carries out extensive global optimization of unconstrained and constrained problems using the recently developed eagle strategy by Yang and Deb in combination with the efficient differential evolution. After a detailed formulation and explanation of its implementation, the proposed algorithm is first verified using twenty unconstrained optimization problems or benchmarks. For the validation against constrained problems, this algorithm is subsequently applied to thirteen classical benchmarks and three benchmark engineering problems reported in the engineering literature. The performance of the proposed algorithm is further compared with various, state-of-the-art algorithms in the area. The optimal solutions obtained in this study are better than the best solutions obtained by the existing methods. The unique search features used in the proposed algorithm are analyzed, and their implications for future research are also discussed in detail.     

基于DE 和SA 的Memetic 高维全局优化算法

针对高维复杂多模态优化问题,传统的进化算法存在收敛速度慢,求解精度低等缺点,提出一种面向高维优化问题的Memetic全局优化算法。算法通过全局搜索和局部搜索结合的混合搜索策略,采用多模式并行差分进化算法进行全局搜索,基于高斯分布估计的模拟退火算法进行局部搜索。改进后的Memetic算法不仅继承了差分进化算法能发现全局最优解的优点,而且能大幅度提高搜索效率。最后,通过对4个高维多峰值Benchmark函数进行仿真实验,实验结果表明本文算法有效提高了算法的收敛速度和求解精度。     

Modified differential evolution approach for optimization of planar parallel manipulators force capabilities

The global optimization problem is not easy to solve and is still an open challenge for researchers since an analytical optimal solution is difficult to obtain even for relatively simple application problems. Conventional deterministic numerical algorithms tend to stop the search in local minimum nearest to the input starting point, mainly when the optimization problem presents nonlinear, non-convex and non-differential functions, multimodal and nonlinear. Nowadays, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. The primary advantage of EAs over other numerical methods is that they just require the objective function values, while properties such as differentiability and continuity are not necessary. In this context, the differential evolution (DE), a paradigm of the evolutionary computation, has been widely used for solving numerical global optimization problems in continuous search space. DE is a powerful population-based stochastic direct search method. DE simulates natural evolution combined with a mechanism to generate multiple search directions based on the distribution of solutions in the current population. Among DE advantages are its simple structure, ease of use, speed, and robustness, which allows its application on several continuous nonlinear optimization problems. However, the performance of DE greatly depends on its control parameters, such as crossover rate, mutation factor, and population size and it often suffers from being trapped in local optima. Conventionally, users have to determine the parameters for problem at hand empirically. Recently, several adaptive variants of DE have been proposed. In this paper, a modified differential evolution (MDE) approach using generation-varying control parameters (mutation factor and crossover rate) is proposed and evaluated. The proposed MDE presents an efficient strategy to improve the search performance in preventing of premature convergence to local minima. The efficiency and feasibility of the proposed MDE approach is demonstrated on a force optimization problem in Robotics, where the force capabilities of a planar 3-RRR parallel manipulator are evaluated considering actuation limits and different assembly modes. Furthermore, some comparison results of MDE approach with classical DE to the mentioned force optimization problem are presented and discussed.     

Development of seven hybrid methods based on collective intelligence for solving nonlinear constrained optimization problems

Many real-world problems can be seen as constrained nonlinear optimization problems (CNOP). These problems are relevant because they frequently appear in many industry and science fields, promoting, in the last decades, the design and development of many algorithms for solving CNOP. In this paper, seven hybrids techniques, based on particle swarm optimization, the method of musical composition and differential evolution, as well as a new fitness function formulation used to guide the search, are presented. In order to prove the performance of these techniques, twenty-four benchmark CNOP were used. The experimental results showed that the proposed hybrid techniques are competitive, since their behavior is similar to that observed for several methods reported in the specialized literature. More remarkably, new best known are identified for some test instances.