共查询到20条相似文献,搜索用时 15 毫秒
1.
基于分而治之的策略,研究求解大规模优化问题的新方法。首先,基于加性可分性原理提出一种改进的变量分组方法,该方法以随机取点的方式,成对检测所有变量之间的相关性;同时,充分利用相关性学习的信息,对可分变量组进行再次降维;其次,引入改进的差分进化算法作为新型子问题优化器,增强了子空间的寻优性能;最后,将两项改进引入到协同进化框架构建DECC-NDG-CUDE算法。在10个选定的大规模优化问题上进行分组和优化两组仿真实验,分组实验结果表明新的分组方法能有效识别变量的相关性,是有效的变量分组方法;优化实验表明,DECC-NDG-CUDE算法对10个问题的求解相对于两种知名算法DECC-DG、DECCG在性能上具备整体优势。 相似文献
2.
Self-adaptive differential evolution with multi-trajectory search for large-scale optimization 总被引:1,自引:0,他引:1
Shi-Zheng Zhao Ponnuthurai Nagaratnam Suganthan Swagatam Das 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2011,15(11):2175-2185
In this paper, self-adaptive differential evolution (DE) is enhanced by incorporating the JADE mutation strategy and hybridized
with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS) to solve large-scale continuous optimization problems.
The JADE mutation strategy, the “DE/current-to-pbest” which is a variation of the classic “DE/current-to-best”, is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover
as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation
in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted
by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation
strategy along with associated parameter settings will be determined adaptively to match different phases of the search process.
MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the
global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every
MMTS step. The success rates of both SaDE and the MMTS are determined and compared; consequently, future function evaluations
for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed
to solve the 19 numerical optimization problems in special issue of soft computing on scalability of evolutionary algorithms
for large-scale continuous optimization problems and competitive results are presented. 相似文献
3.
Scalability of generalized adaptive differential evolution for large-scale continuous optimization 总被引:1,自引:1,他引:0
Zhenyu Yang Ke Tang Xin Yao 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2011,15(11):2141-2155
Differential evolution (DE) has become a very powerful tool for global continuous optimization problems. Parameter adaptations
are the most commonly used techniques to improve its performance. The adoption of these techniques has assisted the success
of many adaptive DE variants. However, most studies on these adaptive DEs are limited to some small-scale problems, e.g. with
less than 100 decision variables, which may be quite small comparing to the requirements of real-world applications. The scalability
performance of adaptive DE is still unclear. In this paper, based on the analyses of similarities and drawbacks of existing
parameter adaptation schemes in DE, we propose a generalized parameter adaptation scheme. Applying the scheme to DE results
in a new generalized adaptive DE (GaDE) algorithm. The scalability performance of GaDE is evaluated on 19 benchmark functions
with problem scale from 50 to 1,000 decision variables. Based on the comparison with three other algorithms, GaDE is very
competitive in both the performance and scalability aspects. 相似文献
4.
差分进化是一种求解连续优化问题的高效算法。然而差分进化算法求解大规模优化问题时,随着问题维数的增加,算法的性能下降,且搜索时间呈指数上升。针对此问题,本文提出了一种新的基于Spark的合作协同差分进化算法(SparkDECC)。SparkDECC采用分治策略,首先通过随机分组方法将高维优化问题分解成多个低维子问题,然后利用Spark的弹性分布式数据模型,对每个子问题并行求解,最后利用协同机制得到高维问题的完整解。通过在13个高维测试函数上进行的对比实验和分析,实验结果表明算法加速明显且可扩展性好,验证了SparkDECC的有效性和适用性。 相似文献
5.
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. 相似文献
6.
Differential evolution (DE) is a simple, yet very effective, population-based search technique. However, it is challenging to maintain a balance between exploration and exploitation behaviors of the DE algorithm. In this paper, we boost the population diversity while preserving simplicity by introducing a multi-population DE to solve large-scale global optimization problems. In the proposed algorithm, called mDE-bES, the population is divided into independent subgroups, each with different mutation and update strategies. A novel mutation strategy that uses information from either the best individual or a randomly selected one is used to produce quality solutions to balance exploration and exploitation. Selection of individuals for some of the tested mutation strategies utilizes fitness-based ranks of these individuals. Function evaluations are divided into epochs. At the end of each epoch, individuals between the subgroups are exchanged to facilitate information exchange at a slow pace. The performance of the algorithm is evaluated on a set of 19 large-scale continuous optimization problems. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that mDE-bES has a competitive performance and scalability behavior compared to the contestant algorithms. 相似文献
7.
Ilhem Boussaïd Amitava Chatterjee Patrick Siarry Mohamed Ahmed-Nacer 《Computers & Operations Research》2011
The present paper proposes a new stochastic optimization algorithm as a hybridization of a relatively recent stochastic optimization algorithm, called biogeography-based optimization (BBO) with the differential evolution (DE) algorithm. This combination incorporates DE algorithm into the optimization procedure of BBO with an attempt to incorporate diversity to overcome stagnation at local optima. We also propose to implement an additional selection procedure for BBO, which preserves fitter habitats for subsequent generations. The proposed variation of BBO, named DBBO, is tested for several benchmark function optimization problems. The results show that DBBO can significantly outperform the basic BBO algorithm and can mostly emerge as the best solution providing algorithm among competing BBO and DE algorithms. 相似文献
8.
Yiqiao Cai Jiahai Wang Jian Yin 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(2):303-330
Differential evolution (DE) is a simple and powerful population-based search algorithm, successfully used in various scientific
and engineering fields. However, DE is not free from the problems of stagnation and premature convergence. Hence, designing
more effective search strategies to enhance the performance of DE is one of the most salient and active topics. This paper
proposes a new method, called learning-enhanced DE (LeDE) that promotes individuals to exchange information systematically.
Distinct from the existing DE variants, LeDE adopts a novel learning strategy, namely clustering-based learning strategy (CLS).
In CLS, there are two levels of learning strategies, intra-cluster learning strategy and inter-cluster learning strategy.
They are adopted for exchanging information within the same cluster and between different clusters, respectively. Experimental
studies over 23 benchmark functions show that LeDE significantly outperforms the conventional DE. Compared with other clustering-based
DE algorithms, LeDE can obtain better solutions. In addition, LeDE is also shown to be significantly better than or at least
comparable to several state-of-art DE variants as well as some other evolutionary algorithms. 相似文献
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10.
针对单种群差分进化算法易出现早熟收敛的问题,提出了一种改进的动态多种群并行差分进化算法。该算法首先利用佳点集方法产生初始种群以增强算法的稳定性和全局搜索能力。基于个体的适应度将种群分为三个子种群,并分别执行采用不同实验向量产生策略和控制参数设置的差分进化算法,既保持了各个子种群算法的独立性和优越性,又不增加算法的复杂性。仿真实验结果表明该算法具有较好的寻优性能。 相似文献
11.
求解函数优化的新型差异演化算法* 总被引:2,自引:1,他引:1
针对差异演化算法存在早熟收敛和后期求解效率低的缺点,提出一种新型差异演化算法。该算法基于单种群,在演化过程中直接对当前种群进行变异、交叉和选择操作,无须差异演化算法中的中间过渡种群。此外,新型差异演化算法的变异与交叉概率是时变的,其中变异概率随着迭代次数的增加而减小;交叉概率随着迭代次数的增加而增加。对几个典型的测试函数进行仿真实验表明,该算法能够有效避免早熟收敛,改善了差异演化算法的优化性能。 相似文献
12.
In this study, we consider the scenario that differential evolution (DE) is applied for global numerical optimization and the index-based neighborhood information of population is used for enhancing the performance of DE. Although many methods are developed under this scenario, neighborhood information of current population has not been systematically exploited in the DE algorithm design. Furthermore, previous studies have shown the effect of neighborhood topology interacted with the function being solved. However, there are few investigations of DE that consider different topologies for different functions during the evolutionary process. Motivated by these observations, a new DE framework, named neighborhood-adaptive DE (NaDE), is presented. In NaDE, a pool of index-based neighborhood topologies is firstly used to define multiple neighborhood relationships for each individual and then the neighborhood relationships are adaptively selected for the specific functions during the evolutionary process. In this way, a more appropriate neighborhood relationship for each individual can be determined adaptively to match different phases of the search process for the function being solved. After that, a neighborhood-dependent directional mutation operator is introduced into NaDE to generate a new solution with the selected neighborhood topology. Being a general framework, NaDE is easy to implement and can be realized with most existing DE algorithms. In order to test the effectiveness of the proposed framework, we have evaluated NaDE via investigating several instantiations of it. Experimental results have shown that NaDE generally outperforms its corresponding DE algorithm on different kinds of optimization problems. Moreover, the synergy among different neighborhood topologies in NaDE is also revealed when compared with the DE variants with single neighborhood topology. 相似文献
13.
针对耗时计算目标函数的约束优化问题,提出用代理模型来代替耗时计算目标函数的方法,并结合目标函数的信息对约束个体进行选择,从而提出基于代理模型的差分进化约束优化算法。首先,采用拉丁超立方采样方法建立初始种群,用耗时计算目标函数对初始种群进行评估,并以此为样本数据建立目标函数的神经网络代理模型。然后,用差分进化方法为种群中的每一个亲本产生后代,并对后代使用代理模型进行评估,采用可行性规则来比较后代与其亲本并更新种群,根据替换机制将种群中较劣的个体替换为备用存档中较优的个体。最后,当达到最大适应度评估次数时算法停止,给出最优解。该算法与对比算法在10个测试函数上运行的结果表明,该算法得出的结果更精确。将该算法应用于工字梁优化问题的结果表明,相较于优化前的算法,该算法的适应度评估次数减少了80%;相对于FROFI(Feasibility Rule with the incorporation of Objective Function Information)算法,该算法的适应度评估次数减少了36%。运用所提算法进行优化可以有效减少调用耗时计算目标函数的次数,提升优化效率,节约计算成本。 相似文献
14.
针对0-1任务规划模型存在维数灾维的问题,提出一种基于改进自适应差分进化(SADE)算法的大规模整数任务分配算法。首先,将任务分配的0-1规划模型转化整数规划模型,不仅大幅减少了优化变量的维数,还减少了整式约束条件;然后,将常用的变异算子DE/rand/1/bin和DE/best/2/bin结合起来组成新的自适应变异算子,使得自适应差分进化算法既有较快的收敛速度,又降低了变异算子对具体问题的依赖;并用改进自适应差分进化算法求解整数规划。最后,通过典型的任务分配实例验证了算法在优化大规模任务分配的有效性和快速性。 相似文献
15.
协同进化算法在求解大规模全局优化问题上具有较好的效果,其核心思想是利用分而治之的策略将一个高维问题分解成若干个子问题,然后分别优化每个子问题.然而,现有的分解方法通常需要花费大量的计算成本来获得精确的变量分组.通过采用递归交互检测中的历史信息简化分组过程,能够避免检测某些集合的相互关系,本文提出了一种新型三层递归差分分组策略(NTRDG).与其他4种现有的分组方法相比, NTRDG在不影响分组精度的情况下计算成本消耗较低.仿真结果表明, NTRDG在求解大规模全局优化问题时具有很强的竞争力. 相似文献
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国民经济非垂直管理行业或领域建立大数据中心,需要配备能大规模云同步归集行业数据的软件系统,“行业数据云通用的同步枢纽与大数据联合体平台”(GSMS)就是为此而研制的。GSMS主要用于通过互联网大规模同步采集各地异构自治系统(或设备)的业务或事实数据并加以开发应用。在实际应用中,当众多GSMS客户线程各自并发地向GSMS数据中心同步数据时,所产生的大规模数据同步会话将汇聚在GSMS服务端,从而形成处理瓶颈。此外,同步会话全程串行的锁步机制也会制约大规模数据同步归集的性能。为此,提出并实现了一种异步并行化改进GSMS系统方案:将服务端高时耗计算环节从数据同步串行锁步过程中分离出来,为其引入基于多道消息队列中间件的异步并行处理机制,并提供相应的松弛同步事务保障措施。实践表明,正确地实现这种异步并行处理能有效提升服务端处理速度并满足同步系统的可靠性和一致性要求。 相似文献
18.
To solve complex antenna design problems, this article proposes a hybrid differential evolution algorithm (DE). The proposed method combines the DE with the simplified quadratic interpolation (SQI) to optimize the performance of the antenna. The DE is the global optimizer, and the SQI is used to fine tune. The hybrid DE is demonstrated on optimizing designing Yagi‐Uda antennas and wideband patch antennas. Numerical results confirm that the proposed method is superior to or at least competitive with the original DE and other evolutionary algorithms in terms of convergence speed and solution quality. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE 2010. 相似文献
19.
提出一种改进的差分进化算法用于求解约束优化问题.该算法在处理约束时不引入惩罚因子,使约束处理问题简单化.利用佳点集方法初始化个体以维持种群的多样性.结合差分进化算法两种不同变异策略的特点,对可行个体与不可行个体分别采用DE/best/1变异策略和DE/rand/1策略,以提高算法的全局收敛性能和收敛速率.用几个标准的Benchmark问题进行了测试,实验结果表明该算法是一种求解约束优化问题的有效方法. 相似文献