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Search Results: 1 - 10 of 305026 matches for " orthogonal design<br>蚂蚁算法 "
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Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems
Xiao-Min Hu Jun Zhang Yun Li,

计算机科学技术学报 , 2008,
Abstract: Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed “continuous orthogonal ant colony” (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an “adaptive regional radius” method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization — API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Supported by the National Natural Science Foundation of China under Grant No. 60573066, the Guangdong Natural Science Foundation Research under Grant No. 5003346, and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, P.R. China.
The Integrated Application of Genetic Algorithm and Rotating Orthogonal Method

QIN Jin,LIANG Liang,

系统工程理论与实践 , 2002,
Abstract: By combining genetic algorithm and rotating orthogonal design, a new algorithm is proposed in this paper. Besides the genetic operations, the best individual of each orthogonal sub feasible area is added into the population. Experiments showed that the new algorithm is more efficient and stable than simple genetic algorithm, especially in multi\|apex problem.
Research on Partners Selection of Collaborative Product Design Chain Based on Ant Algorithm

Wang Youyuan Xu Xinwei Zhou Rigui,

现代图书情报技术 , 2006,
Abstract: Optimizing design chain can cut down the time to market, thus, acquire competitive advantage. Partners selection is very important to collaborative product design chain, the issue of partners selection is a combinational optimize. This paper puts forward a new method based on combination of genetic algorithm and ant algorithm, u- sing genetic algorithm to generate preliminary pheromone distribution for ant algorithm, then ant algorithm searches for optimal result, the result by this method accord with practicality.
Hybrid Self-Adaptive Orthogonal Genetic Algorithm for Solving Global Optimization Problems

JIANG Zhong-Yang,CAI Zi-Xing,WANG Yong,
,蔡自兴,王 勇

软件学报 , 2010,
Abstract: 提出了一种基于正交实验设计的混合自适应正交遗传算法(hybrid self-adaptive orthogonal genetic algorithm,简称HSOGA)以求解全局优化问题,此算法利用正交实验设计方法设计交叉算子,并提出一种自适应正交交叉算子.该自适应正交交叉算子根据父代个体的相似度自适应地调整正交表的因素个数和对父代个体进行因素分割的位置,生成具有代表性的子代个体,以更好地搜索空间.此外,新算法利用自适应正交交叉算子生成均匀分布的初始种群,以保证初始群体的多样性.同时引入了局部搜索策略以提高算法局部搜索能力和收敛速度.通过14个高维的Benchmark函数验证了算法的通用性和有效性.
Multi-objective particle swarm optimizer based on orthogonal design

LIU Yan-min,ZHAO Qing-zhen,NIU Ben,

计算机应用研究 , 2011,
Abstract: In order to improve the ability of PSO in solving multi-objective optmization, by discussing the relationship between method of initialization swarm and algorithm performance,this paper proposed a multi-objective PSO based on orthogonal design (ODMOPSO for short). In the proposed algorithm,firstly,used orthogonal design to initialize the population,which made the algorithm can search in the whole feasible space;next,introduced the comprehensive learning strategy to improve the probability of flying to Paret...
Orthogonal Differential Evolution Algorithm for Solving System of Equations

计算机科学 , 2012,
Abstract: First,nonlinear system of ectuations was transformed into unconstrained optimization problem by using the concept of surrogate constraints and amended maximum entropy function. Then, the concept of average similarity was introduced to design adaptive orthogonal crossover operator, and orthogonal design was used to generate initial populalion, and on the basis, adaptive orthogonal differential evolution algorithm was proposed for solving the maximum entropy function. Finally, using equations verified the algorithm.

WU Shaoyan,ZHANG Qingfu,CHEN Huowang,

软件学报 , 1997,
Abstract: Several popular approaches of simulated evolution have been developed separately. These approaches emphasize different facets of the natural evolutionary processes, respectively. One has recognized that the simulated evolution will benefit from the adequate combination between the approaches. This paper characterizes the primary difference among existing approaches as the difference between genetic link and behavioral link. A new model of simulated evolution, called FEBE(family eugenics based evolution), is proposed, which combines the genetic link with the behavioral link in light of the idea of family eugenics. In the FEBE model the orthogonal design technique is introduced into offspring's breeding inside a family so as to enhance the behavioral improvement of individuals. The FEBE model is applied to solve Goldberg's deceptive problem that is challenging to most evolutionary algorithms. The exciting experimental results are achieved.
A Genetic Algorithm for a Class of Nonlinear Optimization Problems with Interval Coefficients

Advances in Applied Mathematics (AAM) , 2016, DOI: 10.12677/AAM.2016.51017
Abstract: 本文针对一类带区间系数的非线性优化问题,提出了一种基于均匀搜索的遗传算法。首先,将原问题分解为两个确定的双层规划问题;其次,对两个双层问题的上层变量进行编码,通过求解相应的双层规划获得对个体的评估;最后,为避免近亲繁殖产生相似后代,采用相对距离控制杂交运算;并且引进摆动式正交杂交算子产生后代个体,使后代尽可能均匀产生。数据仿真结果表明,该算法是可行有效的。
For a class of nonlinear programming problems with interval coefficients, a genetic algorithm based on a uniformly searching scheme is proposed in this paper. Firstly, the original problem is transformed into two exact bilevel programs. Secondly, the upper level variables are encoded as individuals, and these individuals are evaluated by solving the bilevel programs. Finally, in order to avoid producing similar offspring by inbreeding, a relative distance is adopted to provide a threshold value for crossover. Also, an orthogonal crossover operator with point oscillating is provided to generate offspring as uniformly as possible. The experimental data indicate that this algorithm is feasible and effective.
- , 2017, DOI: 10.12068/j.issn.1005-3026.2017.04.016
Abstract: 摘要 针对砂轮划片机划切工艺参数在实际生产中难以合理设定的问题,提出了基于Matlab遗传算法优化和确定最佳工艺参数的方法.在避开各阶固有频率的基础上选取工艺参数范围,以主轴振动均方根值为评价指标,利用回归正交设计法进行试验,建立了振动量与划切工艺参数之间的回归方程.利用Matlab遗传算法对所建回归方程进行迭代优化,得出对应最小振动量下的最佳工艺参数,对最佳工艺参数下的划切振动量进行试验验证,证明了优化结果的正确性.
Abstract:The cutting process parameters of dicing saw are difficult to get in actual production, an optimization method was thus proposed to establish the best cutting parameters based on genetic algorithm of Matlab. The scope of the cutting process parameters was selected by avoiding the natural frequency of every order, and the root mean square of the axis vibration was used as the evaluation index. The regression equation was established between parameters of vibration and cutting processes by regression orthogonal design. The best cutting parameters according to the minimum vibration were obtained by using the Matlab genetic algorithm to make iterative optimization for the regression equation. The optimization result was verified by experiments.
The Business Rules Engine Inference Approach Based on Orthogonal Multi-agent Genetic Algorithm

ZHANG Lei,ZHANG Rui-Sheng,LI Lian,

计算机科学 , 2007,
Abstract: It uses Forward-Chaining arithmetic to realize inference in business rules engine. It proposes a inference ap- proach of business rules engine based on orthogonal multi-agent genetic algorithm in this paper to solve the limitation that business rules engine can not solve The Winner Determination Approach of Combinatorial Service Compete. Or- thogonal design is introduced to generate an initial population of points that are scattered uniformly over the feasible so- lution space and to generate a crossover operator. It realizes the global optimal computation via the local interacting a- gents with abilities of local perceptivity, competition and evolvement, self-learning etc.
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