共查询到20条相似文献,搜索用时 93 毫秒
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在分析并行多物种遗传算法应用于神经网络拓扑结构的设计和学习之后,提出一种伪并行遗传(PPGA-MBP)混合算法,结合改进的BP算法对多层前馈神经网络的拓扑结构进行优化。算法编码采用基于实数的层次混合方式,允许两个不同结构的网络个体交叉生成有效子个体。利用该算法对N-Parity问题进行了实验仿真,并对算法中评价函数各部分系数和种群规模对算法的影响进行了分析。实验证明取得了明显的优化效果,提高了神经网络的自适应能力和泛化能力,具有全局快速收敛的性能。 相似文献
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作为新兴的智能算法,蝗虫优化算法在作业车间调度问题中的应用符合智能制造的趋势。但由于全局寻优能力不足,基本蝗虫优化算法(GOA)在解决作业车间调度问题(JSP)时容易陷入局部最优,导致收敛精度较低。为了克服上述缺陷,利用量子旋转门操作对其进行改进,提出了一种基于量子计算思想的混合蝗虫优化算法(HGOA)。此外,对混合蝗虫优化算法进行了计算复杂度分析与全局收敛性证明,并利用11个作业车间标准测试问题进行了仿真实验。通过与基本蝗虫优化算法(GOA)、鲸鱼优化算法(WOA)、布谷鸟搜索算法(CS)、灰狼优化算法(GWO)的比较发现,混合蝗虫优化算法在平均值、最小值、寻优成功率及迭代次数方面存在较优结果。研究表明,混合蝗虫优化算法具有更强的全局搜索能力,更好的收敛精度,能够有效跳出局部最优。 相似文献
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针对规划问题,引入了固定结构解的描述形式,指出其离散量与连续量混合的多峰值优化的特点.在此基础上提出了固定结构遗传规划算法(GP)、模拟退火规划算法(SAP),并进行了算法分析.最后通过实验对四个典型优化函数的优化进行了比较研究.研究与实验结果表明SAP算法综合考虑了结构优化与参数优化,具有收敛效率高、获得更优解概率大... 相似文献
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一种模拟退火和粒子群混合优化算法 总被引:3,自引:1,他引:2
针对粒子群优化算法(PSO)容易陷入局部极值点、进化后期收敛慢和优化精度较差等缺点.把模拟退火技术(SA)引入到PSO箅法中,提出了一种混合优化算法.混合优化算法在各温度下依次进行PSO和SA搜索,是一种两层的串行结构.由于PSO提供了并行搜索结构,所以,混合优化算法使SA转化成并行SA算法.SA的概率突跳性保证了种群的多样性,从而防止PSO算法陷入局部极小.混合优化算法保持了PSO算法简单容易实现的特点,改善了算法的全局优化能力,提高了算法的收敛速度和计算精度.仿真结果表明,混合优化算法的优化性能优于基本PSO算法. 相似文献
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针对规划问题,引入了固定结构解的描述形式,指出其离散量与连续量混合的多峰值优化的特点。在此基础上提出了固定结构遗传规划算法(GP)、模拟退火规划算法(SAP),并进行了算法分析。最后通过实验对四个典型优化函数的优化进行了比较研究。研究与实验结果表明SAP算法综合考虑了结构优化与参数优化,具有收敛效率高、获得更优解概率大的特点;GP算法有利于结构优化,但不利于参数优化,具有收敛效率较低,获得更优解的概率较小的特点。 相似文献
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蚁群优化算法(ACO)的正反馈机制使其具有强大的局部搜索性能,但其全局优化性的优劣在很大程度上与挥发系数的选择有关,如选择得不合适则易将使算法陷入局部最优,而禁忌搜索算法(TS)则具有强大的全局优化性能。为了弥补单一ACO算法的局限性,将ACO算法与TS算法组合起来,提出了基于TS和ACO算法的混合优化算法HTSACO,并将该混合优化算法用于求解最大独立集问题。实验表明:与标准蚁群优化算法相比,该算法显示出了很高的全局优化性和计算效率。 相似文献
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提出一种基于梯度下降法的混合进化算法,用于确定径向基函数(RBF)神经网络结构和优化其参数.在进化算法中嵌入梯度下降算子,对每一代中若干个精英个体以一定概率利用梯度下降法进行搜索,以加强算法的局部搜索能力.利用混合进化算法对RBF网络结构和参数同时进行训练和优化,对网络节点数和参数进行混合编码.仿真实验结果表明该RBF网络具有较强的泛化能力. 相似文献
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机器学习的性能受特征选择和参数优化的影响很大,针对这一问题,采用基于蚁群算法和遗传算法的混合算法对特征选择和参数优化问题进行了探究。实验结果表明,该混合算法相比单个的蚁群算法或遗传算法,在特征选择和参数优化方面,具有更高的准确率。 相似文献
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The twin-screw configuration problem (TSCP) arises in the context of polymer processing, where twin-screw extruders are used to prepare polymer blends, compounds or composites. The goal of the TSCP is to define the configuration of a screw from a given set of screw elements. The TSCP can be seen as a sequencing problem as the order of the screw elements on the screw axis has to be defined. It is also inherently a multi-objective problem since processing has to optimize various conflicting parameters related to the degree of mixing, shear rate, or mechanical energy input among others. In this article, we develop hybrid algorithms to tackle the bi-objective TSCP. The hybrid algorithms combine different local search procedures, including Pareto local search and two phase local search algorithms, with two different population-based algorithms, namely a multi-objective evolutionary algorithm and a multi-objective ant colony optimization algorithm. The experimental evaluation of these approaches shows that the best hybrid designs, combining Pareto local search with a multi-objective ant colony optimization approach, outperform the best algorithms that have been previously proposed for the TSCP. 相似文献
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Dynamic optimization problems are a kind of optimization problems that involve changes over time.They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time.Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years.Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments.One approach is to maintain the diversity of the population via random immigrants.This paper proposes a hybrid immigrants scheme that combines the concepts of elitism,dualism and random immigrants for genetic algorithms to address dynamic optimization problems.In this hybrid scheme,the best individual,i.e.,the elite,from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme.These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population,replacing the worst individuals in the population.These three kinds of immigrants aim to address environmental changes of slight,medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes.Based on a series of systematically constructed dynamic test problems,experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme.Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments. 相似文献
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Metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. However, the choice of a particular algorithm to optimize a certain problem is still mainly driven by some sort of devotion of its author to a certain technique rather than by a rationalistic choice driven by reason. Hybrid algorithms have shown their ability to provide local optima of high quality. Hybridization of algorithms is still in its infancy: certain combinations of algorithms have experimentally shown their performance, though the reasons of their success is not always really clear. In order to add some rational to these issues, we study the structure of search spaces and attempt to relate it to the performance of algorithms. We wish to explain the behavior of search algorithms with this knowledge and provide guidelines in the design of hybrid algorithms. This paper briefly reviews the current knowledge we have on search spaces of combinatorial optimization problems. Then, we discuss hybridization and present a general classification of the way hybridization can be conducted in the light of our knowledge of the structure of search spaces. 相似文献
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Optimization of one-dimensional Bin Packing Problem with island parallel grouping genetic algorithms
The well-known one-dimensional Bin Packing Problem (BPP) of whose variants arise in many real life situations is a challenging NP-Hard combinatorial optimization problem. Metaheuristics are widely used optimization tools to find (near-) optimal solutions for solving large problem instances of BPP in reasonable running times. With this study, we propose a set of robust and scalable hybrid parallel algorithms that take advantage of parallel computation techniques, evolutionary grouping genetic metaheuristics, and bin-oriented heuristics to obtain solutions for large scale one-dimensional BPP instances. A total number of 1318 benchmark problems are examined with the proposed algorithms and it is shown that optimal solutions for 88.5% of these instances can be obtained with practical optimization times while solving the rest of the problems with no more than one extra bin. When the results are compared with the existing state-of-the-art heuristics, the developed parallel hybrid grouping genetic algorithms can be considered as one of the best one-dimensional BPP algorithms in terms of computation time and solution quality. 相似文献
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一类GASA混合策略及其收敛性研究 总被引:18,自引:2,他引:18
结合模拟退火算法(SA)和遗传算法(GA)提出一类GASA混合优化策略,借助于非平稳马氏链理论证明混合算法的全局渐近收敛性,同时实性地分析了算法的优化效率。 相似文献
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Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm 总被引:1,自引:0,他引:1
Ali R. Yıldız Nursel Öztürk Necmettin Kaya Ferruh Öztürk 《Structural and Multidisciplinary Optimization》2007,34(4):317-332
This research is based on a new hybrid approach, which deals with the improvement of shape optimization process. The objective
is to contribute to the development of more efficient shape optimization approaches in an integrated optimal topology and
shape optimization area with the help of genetic algorithms and robustness issues. An improved genetic algorithm is introduced
to solve multi-objective shape design optimization problems. The specific issue of this research is to overcome the limitations
caused by larger population of solutions in the pure multi-objective genetic algorithm. The combination of genetic algorithm
with robust parameter design through a smaller population of individuals results in a solution that leads to better parameter
values for design optimization problems. The effectiveness of the proposed hybrid approach is illustrated and evaluated with
test problems taken from literature. It is also shown that the proposed approach can be used as first stage in other multi-objective
genetic algorithms to enhance the performance of genetic algorithms. Finally, the shape optimization of a vehicle component
is presented to illustrate how the present approach can be applied for solving multi-objective shape design optimization problems. 相似文献
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基于遗传算法的双目标车辆路线优化研究 总被引:2,自引:0,他引:2
本文对车辆路线优化问题建立了双目标多旅行商问题模型,提出一种求解旅行商问题混合遗传算法,并对双目标多旅行商问题提出了解决方案。基于实例的仿真结果表明,文章提出的算法和解决方案是可行而有效的。 相似文献
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An improved GA and a novel PSO-GA-based hybrid algorithm 总被引:2,自引:0,他引:2
Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the “dying probability” for the individuals and the “war/disease process” for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Simulation results show that both VPGA and PGHA are effective for the optimization problems. 相似文献