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1.
周雅兰  徐志 《计算机科学》2015,42(6):247-250, 255
差分演化(Differential Evolution,DE)算法的性能依赖于变异策略的选择和控制参数的设置.不同问题对DE的变异策略和参数的设置各不相同.为了提高DE的性能,提出一种多变异策略的自适应差分演化算法,建立由多种变异策略组成的策略池,两个主要参数自适应策略控制.为了验证所提算法的性能,在测试数据集CEC2013上进行了实验,并将其与使用6种不同变异策略的原始DE和4种改进DE进行比较.实验结果表明,提出的算法是一种有效的DE变种,其性能优于其它DE.  相似文献   

2.
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.  相似文献   

3.
差分演化算法(differential evolution,DE)是一种模拟生物演化过程的随机搜索方法,具有收敛速度快,鲁棒性好等优点。目前DE有多种交叉和变异策略,它们在求解各类优化问题时表现出各自不同的性能。介绍了10种差分演化算法的更新策略,并利用标准测试函数集对它们进行了全面与系统的实验比较。通过分析采用这些策略的DE算法在不同解空间及进化各阶段的收敛曲线特点,对比总结了不同版本的DE算法在各类环境下的搜索性能。该研究一方面能够为DE算法的实际应用提供技术指导,帮助学者选择合适的DE更新策略以更好地解决工程问题;另一方面能够为新型DE更新策略的开发和自适应DE算法的设计提供理论基础。  相似文献   

4.
标准差分进化算法(SDE)具有算法简单,控制参数少,易于实现等优点.但在难优化问题中,算法存在收敛速度较慢和容易早熟等缺陷.为克服此缺点,提出一种改进算法——双种群差分进化规划算法(BGDEP).该算法将种群划分为两个子群独立进化,分别采用DE/rand/1/bin和DE/best/2/bin版本生成变异个体.每隔δt(取5~10)代,将两个子群合并为一个种群,再应用混沌重组算子将之划分为两个子群,以实现子群间的信息交流.在双种群协同差分进化的同时,应用非均匀变异算子对其最优个体执行进化规划操作,使得算法具有较快的收敛速度和较强的全局寻优能力.为测试BGDEP的性能,给出了4个30维benchmark函数优化问题的对比数值实验.结果表明,BGDEP的求解精度、收敛速度、鲁棒性等性能优于SDE、双种群差分进化(BGDE)和非均匀变异进化规划(NUMEP)等4种算法.  相似文献   

5.
粒子群优化方法(Particle Swarm Optimization,PSO)是由Kennedy和Eberhart于1995年提出的一种基于群体智能(Swarm Intelligence)的演化计算技术,用于求解各类优化问题。PSO方法通过各种参数控制粒子的运行轨迹,并对参数设置有很强的敏感性。因此,如何为PSO方法选择最优的参数是PSO方法的关键。本文提出了一种不依赖个人经验的参数选则策略,针对特定问题,将PSO方法的性能表示成参数的函数,从而将参数选择问题转变成函数优化问题。采用微分演化(Differential Evolution,DE)方法对该函数进行优化,来确定PSO的最佳参数,收到了较好的效果。  相似文献   

6.
刘建  黄文奇 《信息与控制》2006,35(1):103-107
提出了一种改进的微分进化算法(DE)求解二维带平衡约束的圆形packing问题.首先,构造出等价的物理模型,定义系统的能量函数,再对能量函数进行全局优化,从而间接得到问题的近似解.其中引入的参数动态调整策略在计算初期维持个体的多样性,后期加快算法的收敛速度,提高了DE算法的性能.最后,对两个算例进行了数值计算,实验结果证明了算法的有效性.此算法思路可推广应用于求解其它类型布局问题.  相似文献   

7.
赵洋  贺毅朝  李晰 《计算机应用》2012,32(10):2911-2915
在分析差分演化(DE)进化方式基础上,首先利用自加速性改进差异算子与选择算子,然后结合变邻域搜索改善算法的局部搜索能力,提出了一种具有自加速特性与变邻域搜索能力的差分演化算法(SAVNDE);基于DE的三种进化模式,利用5个Benchmark测试函数进行对比计算,实验结果表明:SAVNDE在保持了DE原有特性基础上,以较快的速度获得更好的结果。  相似文献   

8.
求解旅行商问题的位置-次序编码差分演化算法   总被引:1,自引:0,他引:1  
首先利用“差异算子”和“选择算子”描述了差分演化算法(DE)的基本原理,然后提出了一种新的、通用的特殊编码方法:位置 次序编码法,并利用此编码方法,提出了求解著名旅行商问题的离散差分演化算法:基于位置 次序编码的差分演化算法(PODE)。对于TSPLIB中两个不同规模的旅行商问题实例的计算表明,PODE算法具有极好的收敛性和稳定性  相似文献   

9.
基于DE算法的非线性预测控制及其应用   总被引:1,自引:0,他引:1  
针对非线性预测控制系统中需要实时求解非线性规划问题,基于差异演化算法(DE)提出了一种非线性预测控制的新方法。差异演化算法是进化算法的一种,具有全局最优、稳健性强、收敛速度快、及参数调整简单等优点,用于求解非线性预测控制问题中的非线性规划问题具有明显优势。该算法应用于化工过程和化学反应的pH值控制仿真中,仿真结果说明了该方法的有效性。  相似文献   

10.
差分演化的收敛性分析与算法改进   总被引:1,自引:0,他引:1  
为了分析差分演化(differential evolution,简称DE)的收敛性并改善其算法性能,首先将差分算子 (differential operator,简称DO)定义为解空间到解空间的笛卡尔积的一种随机映射,利用随机泛函理论中的随机压缩 映射原理证明了DE 的渐近收敛性;然后,在“拟物拟人算法”的启发下,通过对DE 各进化模式的共性特征与性能差 异的分析,提出了一种具有多进化模式协作的差分演化算法(differential evolution with multi-strategy cooperatingevolution,简称MEDE),分析了它所具有的隐含特性,并在多模式差分算子(multi-strategy differential operator,简称 MDO)定义的基础上证明了它的渐进收敛性.对5 个经典测试函数的仿真计算结果表明,与原始的DE,DEfirDE 和 DEfirSPX 等算法相比,MEDE 算法在求解质量、适应性和鲁棒性方面均具有较明显的优势,非常适于求解复杂高维 函数的数值最优化问题.  相似文献   

11.
This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differential evolution (SDE). In the SDE, population is divided into several memeplexes and each memeplex is improved by the differential evolution (DE) algorithm. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the DE which, on one hand, use the partitioning and shuffling concepts of SDE to compensate for the limited amount of search moves of the original DE and, on the other hand, employ the OBL to accelerate the DE without making premature convergence. Four versions of DE algorithm are proposed based on the OBL and SDE strategies. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments on 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005 and non-parametric analysis of obtained results demonstrate that the performances of the proposed algorithms are better than the SDE. The fourth version of proposed algorithm has a significant difference compared to the SDE in terms of all considered aspects. The emphasis of comparison results is to obtain some successful performances on unsolved functions for the first time, which so far have not been reported any successful runs on them. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE algorithms reported in the literature confirm a significantly better performance of our proposed algorithm, especially on high-dimensional functions.  相似文献   

12.
Differential evolution (DE) is one simple and effective evolutionary algorithm (EA) for global optimization. In this paper, three modified versions of the DE to improve its performance, to repair its defect in accurate converging to individual optimal point and to compensate the limited amount of search moves of original DE are proposed. In the first modified version called bidirectional differential evolution (BDE), to generate a new trial point, is used from the bidirectional optimization concept, and in the second modified version called shuffled differential evolution (SDE), population such as shuffled frog leaping (SFL) algorithm is divided in to several memeplexes and each memeplex is improved by the DE algorithm. Finally, in the third modified version of DE called shuffled bidirectional differential evolution (SBDE) to improve each memeplex is used from the proposed BDE algorithm. Three proposed modified versions are applied on two types of DE and six obtained algorithms are compared with original DE and SFL algorithms. Experiments on continuous benchmark functions and non-parametric analysis of obtained results demonstrate that applying bidirectional concept only improves one type of the DE. But the SDE and the SBDE have a better success rate and higher solution precision than original DE and SFL, whereas those are more time consuming on some functions. In a later part of the comparative experiments, a comparison of the proposed algorithms with some modern DE and the other EAs reported in the literature confirms a better or at least comparable performance of our proposed algorithms.  相似文献   

13.
参数选择本身是一个组合优化问题,尽管过去提出了很多方法,但是参数选择依然令人困惑,为此提出适用于差分进化算法的参数自适应选择策略。该策略在进化的过程中动态评估参数的性能,并根据其结果指导下一次迭代过程的参数选择。从参数库的建立、参数评分机制和参数配置机制3方面展开研究,对比实验结果表明,该方法效果良好。  相似文献   

14.
The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solution is computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential evolution (SDE), an advanced version of differential evolution (DE), is proposed. SDE is a fusion of three algorithmic concepts proposed in modified versions of DE. It utilizes two criteria (1) entropy and (2) approximation of normalized histogram of an image by a mixture of Gaussian distribution to find the optimal thresholds. The experimental results show that SDE can make optimal thresholding applicable in case of multi-level thresholding and the performance is better than some other multi-level thresholding methods.  相似文献   

15.
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.  相似文献   

16.
Recently, evolutionary algorithm based on decomposition (MOEA/D) has been found to be very effective and efficient for solving complicated multiobjective optimization problems (MOPs). However, the selected differential evolution (DE) strategies and their parameter settings impact a lot on the performance of MOEA/D when tackling various kinds of MOPs. Therefore, in this paper, a novel adaptive control strategy is designed for a recently proposed MOEA/D with stable matching model, in which multiple DE strategies coupled with the parameter settings are adaptively conducted at different evolutionary stages and thus their advantages can be combined to further enhance the performance. By exploiting the historically successful experience, an execution probability is learned for each DE strategy to perform adaptive adjustment on the candidate solutions. The proposed adaptive strategies on operator selection and parameter settings are aimed at improving both of the convergence speed and population diversity, which are validated by our numerous experiments. When compared with several variants of MOEA/D such as MOEA/D, MOEA/D-DE, MOEA/D-DE+PSO, ENS-MOEA/D, MOEA/D-FRRMAB and MOEA/D-STM, our algorithm performs better on most of test problems.  相似文献   

17.

The performance of differential evolution (DE) algorithm highly depends on the selection of mutation strategy. However, there are six commonly used mutation strategies in DE. Therefore, it is a challenging task to choose an appropriate mutation strategy for a specific optimization problem. For a better tackle this problem, in this paper, a novel DE algorithm based on local fitness landscape called LFLDE is proposed, in which the local fitness landscape information of the problem is investigated to guide the selection of the mutation strategy for each given problem at each generation. In addition, a novel control parameter adaptive mechanism is used to improve the proposed algorithm. In the experiments, a total of 29 test functions originated from CEC2017 single-objective test function suite which are utilized to evaluate the performance of the proposed algorithm. The Wilcoxon rank-sum test and Friedman rank test results reveal that the performance of the proposed algorithm is better than the other five representative DE algorithms.

  相似文献   

18.
Differential evolution (DE) algorithm is a population based stochastic search technique widely applied in scientific and engineering fields for global optimization over real parameter space. The performance of DE algorithm highly depends on the selection of values of the associated control parameters. Therefore, finding suitable values of control parameters is a challenging task and researchers have already proposed several adaptive and self-adaptive variants of DE. In the paper control parameters are adapted by levy distribution, named as Levy distributed DE (LdDE) which efficiently handles exploration and exploitation dilemma in the search space. In order to assure a fair comparison with existing parameter controlled DE algorithms, we apply the proposed method on number of well-known unimodal, basic and expanded multimodal and hybrid composite benchmark optimization functions having different dimensions. The empirical study shows that the proposed LdDE algorithm exhibits an overall better performance in terms of accuracy and convergence speed compared to five prominent adaptive DE algorithms.  相似文献   

19.
Differential evolution (DE) is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.  相似文献   

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