共查询到17条相似文献，搜索用时 221 毫秒
1.
为了改善人工免疫多目标进化算法的分布性,引入聚集密度以进行Pareto最优解集的更新。其基本思想为：首先计算群体中每个个体的聚集密度,再根据目标函数值和聚集密度定义一个偏序集,然后采用比例选择原则依次从偏序集中选择个体,更新精英集。通过数值实验,用量化指标研究了新算法的收敛性和分布性,结果表明：新算法的收敛性与常规人工免疫多目标进化算法相当,但分布性有了明显提高。 相似文献
2.
针对排序选择法中广泛采用的线性选择方法的缺陷,提出了一种非线性选择方法。这种选择方法既充分体现了非劣解集对劣解集的优先选择权,又考虑到了非劣解集和劣解集中个体的平等性。理论分析和仿真计算表明,这种新的排序选择法不仅能得到分布广泛的Pareto最优解,而且进化速度极快,一般只需30-50代。 相似文献
3.
多目标进化算法有两个重要研究内容：最优解集的构造和解的分布性。用擂台赛法则构造非支配集具有较高的效率,而聚集密度方法既能从宏观上刻画群体的多样性与分布性,同时也比较好地刻画了个体之间的内在关系。将聚集密度技术引入基于擂台赛法则的多目标进化算法。数值计算表明,这种新的算法既保持了擂台赛法则较高的运行速度,又改善了群体的分布度,提高了种群的多样性,避免了过早收敛于局部最优解的现象。 相似文献
4.
为了改善协同进化多目标优化算法性能,引入了聚集密度对超级个体集合进行更新。其基本思想是：首先计算种群中各个体的聚集密度,再定义一个偏序集,然后根据一定的比例依次从偏序集中选择个体更新。根据数值试验和量化指标测试了新算法的收敛性与分布性。结果表明,新算法在收敛性方面与常规协同进化多目标算法相当,但其分布性获得了一定程度的改善。 相似文献
5.
建立了动态车辆路径优化问题的数学模型,提出了一种基于聚集密度的人工免疫多目标进化算法。该算法首先计算群体中每个个体的聚集密度,再根据目标函数值和聚集密度定义一个偏序集,然后采用比例选择原则依次从偏序集中选择个体,更新精英集。实验结果表明,该算法是解决动态车辆路径问题的有效方法。 相似文献
6.
提出一种基于多叉树Pareto最优解集的火灾扑救路径规划算法,对火灾现场的环境地图和火灾演化态势进行重构,实现对路径的优选,采用Pareto最优解集,构建基于多叉树Pareto最优解集的火源动态发展态势下的火灾扑救路径规划模型。实验结果表明,该模型能快速实现对火源热点的识别,并且规划路径能有效规避复杂建筑障碍物的干扰,实现对火灾扑救路径的最优选择。在动态未知环境中,对火灾扑救路径的规划和选择能达到最优,路径最短,分段较少,能有效地避免复杂建筑物的阻挡,有效节省了火灾扑救时间。 相似文献
7.
针对传统遗传算法在巡回商旅问题优化计算中存在的弊端——收敛速度慢,迭代次数多。在传统遗传算法基础上,设计出一种加入人工选择和定向突变的优化改进算法。该优化算法通过人工方法保存具有有利变异个体和淘汰具有不利变异个体,有利变异个体进行杂交和变异,从而提高遗传算法的收敛速度,减少遗传算法的迭代次数。同时针对遗传算法易陷入局部最优解的情况,在优化算法中引入自适应参数算法,针对遗传算法的不同阶段,实现杂交概率和变异概率的自适应调节,防止算法陷入局部最优解。最后,采用国际标准的TSP测试集(TSPLIB)对优化算法的优良性进行验证,实验表明,对比其他算法,该优化算法在TSP最优解的质量上提高10%左右。 相似文献
8.
最优解集的构造和解的分布性是多目标进化算法的两个重要研究内容。用擂台赛法则构造非支配集具有较高的效率,而小生境共享技术可以提高种群的多样性。本文将小生境共享技术引入基于擂台赛法则的多目标进化算法,数值实验表明：改进后的算法保持了擂台赛算法运行效率高的特点,而且具有较佳的分布度。 相似文献
9.
建立了供水调度模型,利用基于分解的多目标进化算法,首先将供水调度问题分解为若干单目标,然后根据分布估计的思想对各个单目标建立概率模型,通过采样产生新的个体。利用非支配排序法进行选择,得到最优解。实验表明,该算法对求解供水调度优化问题具有较好的多样性和均匀性,并且降低了算法的计算复杂度。 相似文献
10.
棉花苗期蚜虫属聚集分布,利用空间格局参数、k、a、β、b等值分析了聚集程度、原因;根据分布信息应用Iwao法,k_c值求得理论抽样数模型,从而导出了当t=1时的不同密度,不同允许误差(D)下的理论抽样数。利用b参数,依公式(Robert等,1975)来估计棉蚜理论数得出的数值偏大,不适用于大田抽样。本文将Iwao和willson(1983)两个序贯抽样模型集合成棉苗蚜虫的Iwao-willson复合序贯抽样法,优化了序贯抽样方法。 相似文献
11.
Application of non-dominated sorting genetic algorithm-II technique for optimal FACTS-based controller design 总被引：1，自引：0，他引：1
Sidhartha Panda 《Journal of The Franklin Institute》2010,347(7):1047-1064
Design of an optimal controller requires optimization of multiple performance measures that are often noncommensurable and competing with each other. Design of such a controller is indeed a multi-objective optimization problem. Non-dominated sorting in genetic algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems. This paper investigates the application of NSGA-II technique for the design of a flexible AC transmission system (FACTS)-based controller. The design objective is to improve the stability of the power system with minimum control effort. The proposed technique is applied to generate Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Further, a detailed analysis on the selection of control signals (both local and remote signals) on the effectiveness of the proposed controller is carried out and simulation results are presented under various loading conditions and disturbances to show the effectiveness and robustness of the proposed approach. 相似文献
12.
《Information processing & management》2023,60(1):103123
Aligning time series of different sampling rates is an important but challenging task. Current commonly used dynamic time warping methods usually suffer from pathological temporal singularity problem. In order to overcome this, we transform the alignment task to a spatial-temporal multi-objective optimization (MOO) problem. Existing MOO algorithms are always inefficient in finding Pareto optimal alignment solutions due to their insufficiency in maintaining convergence and diversity among the obtained Pareto solutions. In light of this, we propose a novel and efficient MOO algorithm Cell-MOWOA which integrates Cellular automata with the rising Whale Optimization Algorithm to find Pareto optimal alignment solutions. Innovative multi-variate non-linear cell state evolutionary rules are designed within Pareto solution external archive to improve the convergence and diversity of the Pareto solutions, and novel whale population updating mechanism is designed to accelerate the convergence to the Pareto front. Besides, new integer whale updating mechanism is presented to transform real-number whale solutions to integer whale solutions. Experimental results on 85 gold-standard UCR time series datasets showed that Cell-MOWOA outperformed six state-of-the-art baselines by 24.53% in average in increasing alignment accuracy and 42.66% in average in reducing singularity. Besides, it achieved outstanding runtime efficiency, especially on long time series datasets. 相似文献
13.
针对庄家算法的缺陷,提出了一种基于信息熵的庄家算法。其基本思想是：在使用庄家算法进行非支配解的选取前,先对群体的信息熵值进行计算。若熵值较低,即没有相对较好的分布度,则对群体进行遗传选择、交叉和变异操作,生成新的群体,直到熵值达到要求,再使用庄家法则进行计算。数值计算表明,这种新的算法既保持了庄家算法较高的收敛速度,又改善了群体的分布度,提高了种群的多样性,避免了过早收敛于局部最优解的现象。 相似文献
14.
Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user’s interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm. 相似文献
15.
《Journal of The Franklin Institute》2021,358(16):8288-8307
This article is concerned with the infinite horizon stochastic cooperative linear-quadratic (LQ) dynamic difference game in both the regular and the indefinite cases. Firstly, due to the constraints imposed on the weighting matrices and the linearity of the dynamic system, the costs are shown to be convex spontaneously for the regular stochastic cooperative LQ difference game, which yields the equivalence between the minimization of the weighted sum of costs and the Pareto optimal control. Secondly, the Pareto optimal control is derived for the regular game on the ground of the solution to the weighted algebraic Riccati equation (WARE) under exact observability, and then Pareto solutions are identified via the optimal feedback gain matrices and the solution to the weighted algebraic Lyapunov equation (WALE). Moreover, a new criterion which is also necessary and sufficient is developed to guarantee the costs to be convex for the indefinite case, and the Pareto optimality is investigated based on the solutions to the weighted generalized algebraic Riccati equation (WGARE) and the weighted generalized algebraic Lyapunov equation (WGALE) combining with the semidefinite programming (SDP). Finally, the fishery management game in the economy is presented to illustrate the obtained results. 相似文献
16.
This paper is concerned with the linear quadratic (LQ) Pareto game of the stochastic singular systems in infinite horizon. Firstly, the optimal control problem of the weighted sum cost functional is discussed. Utilizing the equivalent transformation method, the weighted sum LQ optimal control problem is transformed into a stochastic LQ optimization problem. Based on the classical stochastic LQ optimal control theory, the necessary and sufficient condition for the solvability of the indefinite weighted sum LQ optimal control is put forward. Then, the LQ Pareto game of the stochastic singular systems is studied. By the discussion of the convexity of the cost functionals, a sufficient condition for the existence of the Pareto solutions is obtained via the solvability of the corresponding generalized algebraic Riccati equation (GARE). Moreover, we derive all Pareto solutions based on the solution of a Lyapunov equation. Finally, an example is given to show the effectiveness of the proposed results. 相似文献
17.
一种新的椭球算法 总被引：2，自引：0，他引：2
基于更动约束的思想[1 ] 与方法 ,提出了求解线性规划问题的新椭球算法 .它与L .G .Khachian的椭球算法[2 ] 不同 ,在新算法的椭球迭代过程中 ,不仅用约束不等式割掉不含约束集的半个椭球 (椭球中心不在约束集内时 ) ,称之为约束割 ;而且在椭球中心落在约束集内时 ,它用目标不等式割掉含约束集的半个椭球 ,称之为目标割 .新算法的不等式系统是由原规划 (或对偶规划 )的约束不等式与目标不等式组成的 (规模小 ) ,而不是由原椭球算法的K K T条件[5] 组成的不等式系统 (规模大 ) .这种新椭球算法即有多项式计算复杂性的特性 ,又在迭代过程中得到一系列单调趋向最优解的可行解 (在解存在时 ) .如果认为已得满意解 ,可随时停机 .对于实际问题 ,大多数是变量有界的 ,初始椭球不大 ,因此新算法更为实际 ,有效 . 相似文献