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1.
基于生态毒理动力学模型构造出可全局收敛的函数优化算法。在该算法中,将优化问题的搜索空间看成一个存在污染现象的环境系统,将一个试探解看成一个种群,采用生态毒理动力学模型对种群生长特征的变化规律进行描述。种群在污染作用下不断发生变化,能够抵抗住污染的强壮种群能够获得生长,而无法抵抗住污染的虚弱种群则停止生长。用环境和种群以及种群与种群之间的相互作用关系构造进化算子,这些算子从多种角度实现了种群之间的信息交换。因环境污染影响的是种群的很少部分特征,当种群演化时,只涉及到很少一部分种群特征参与运算,故提高了算法的收敛速度。测试结果表明本算法的精度和性能优于已有的群智能优化算法。  相似文献   

2.
《计算机科学与探索》2019,(9):1567-1581
为了解决一类函数优化问题,利用带时滞影响的混杂食物链微生物培养动力学理论提出一种微生物动力学优化(MDO)算法。在该算法中,假设有多个微生物种群在一个培养系统中培养,微生物种群的生长不但受注入到培养系统中的培养液流量、营养物质和有害物质的浓度影响,而且受种群之间相互作用的影响;定期注入的培养液会突然增加营养物质和有毒物质的浓度,从而会突然加大对种群的影响。利用上述特点构造出了吸收算子、攫取算子、混杂算子和毒素算子;利用这些算子和种群的生长变化,能够快速求解优化问题的全局最优解。仿真实验结果表明,MDO算法对求解维数较高的优化问题具有一定的优势。  相似文献   

3.
基于3种群Lotka-Volterra模型构造出了可全局收敛的种群动力学优化算法。在该算法中,每个种群对应着优化问题的一个试探解;基于3种群间的每种相互作用关系,提出了相应的图形表示方法以及对应的Lotka-Volterra模型构建方法,种群间的相互作用关系包括竞争关系、互惠共存关系、捕食-被食关系或者它们间的任意组合;3种群间的每种相互作用关系均对应着一种种群进化算子,该算子的数学表达式就是其对应的Lotka-Volterra模型的离散化表达式;另外,为了求解更复杂的优化问题求解,将种群融合、突变和选择等行为也构造成操作算子。所有算子的特性可以确保整个种群的适应度指数要么保持原状不变,要么向好的方向转移,从而确保了算法的全局收敛性;在种群演变过程中,种群从一种状态转移到另一种状态实现了种群对优化问题最优解的搜索。应用可归约随机矩阵的稳定性条件证明了本算法具有全局收敛性。测试结果表明本算法是高效的。  相似文献   

4.
基于梯度优化的自适应小生境遗传算法   总被引:1,自引:0,他引:1  
针对基本遗传算法全局搜索能力差和收敛速度慢,且在求解多峰函数时仅能得到部分最优解的缺点,提出一种基于梯度优化的自适应小生境算法。该算法利用当前种群适应度和种群代数来设计交叉算子和变异算子,有效地保持了种群的多样性,改善全局搜索能力,加快了收敛速度,应用改进的梯度优化算子保证进化向最优解方向靠近,提高了计算峰值的精确度。对Shubert函数的仿真试验证明,该算法能改善全局搜索能力,加快算法收敛速度并提高计算精度。  相似文献   

5.
种群动力学优化算法   总被引:2,自引:1,他引:1  
黄光球  李涛  陆秋琴 《计算机科学》2013,40(11):280-286
为了快速求解大规模复杂优化问题,基于种群动力学理论构造出了可全局收敛的种群动力学优化算法。在该算法中,每个种群对应着优化问题的一个试探解,种群的一个特征对应于试探解的一个变量;采用正交拉丁方原理构造出了种群初始值确定方法,以达到对搜索空间的均衡分散性和整齐可比性覆盖;将任意两种群间的竞争、互利、捕食-被食、融合、突变和选择等行为用于构造种群的进化策略,以使种群的适应度指数要么保持原状不变,要么向好的方向转移,从而确保整个算法的全局收敛性;在种群演变过程中,种群从一种状态转移到另一种状态,实现了种群对优化问题全局最优解的搜索。应用可归约随机矩阵的稳定性条件证明了本算法具有全局收敛性。测试结果表明本算法是高效的。  相似文献   

6.
为了求解一类复杂非线性优化问题的全局最优解,基于采用垂直结构群落动力学理论,提出了一种新的垂直结构群落系统优化算法,简称为VS-CSO算法。该算法将优化问题的搜索空间视为一个生态系统,该生态系统具有若干个垂直结构分叉营养水平,在各个营养水平中生活着不同种类的生物种群;在每个种群内,有若干生物个体在活动;生物个体不能跨种群迁移,但在同类种群中会相互影响。各种群以循环捕食-被食或资源-消耗连接在一起。运用垂直结构群落动力学模型开发出了通吃算子、择食算子、干扰算子、侵染算子、新生算子、死亡算子。其中,通吃算子和择食算子可实现个体跨种群的信息交换,而干扰算子和侵染算子可实现种群内部个体之间的信息交换,从而确保个体间信息的充分交换;新生算子可适时补充新个体到种群中,而死亡算子可将种群中的虚弱个体适时清除掉,从而大幅提升算法跳出局部陷阱的能力。在求解过程中,VS-CSO算法每次只对极少变量进行处理,因此可求解高维优化问题。测试结果表明,VS-CSO算法能求解一类非常复杂的单峰函数、多峰函数和复合函数优化问题,其求精能力、探索能力及两者的协调性均优良,且具有全局收敛性的特点。该算法为求解一些较高维复杂函数优化问题的全局最优解提供了可行方案。  相似文献   

7.
用于全局优化的混合正交遗传算法   总被引:7,自引:1,他引:6       下载免费PDF全文
为提高正交遗传算法收敛速度和搜索精度,在正交遗传算法的基础上引入局部搜索策略,提出一种新的聚类局部搜索算子。利用正交算子初始化种群,保证初始群体分布的均匀性和多样性。通过正交算子在全局范围内进行全局搜索,使算法能在全局范围内收敛。采用聚类局部搜索算子对群体进行局部搜索,以增强算法的收敛速度和搜索精度。对7个高维的Benchmark函数进行测试,仿真实验结果表明,与其他算法相比,该算法具有更好的搜索精度、收敛速度和全局寻优的能力。  相似文献   

8.
为了解决进化算法在求解全局优化时易陷入局部最优和收敛速度慢的问题,设计了一个杂交算子,利用种群中最好点与其他点间的关系确定搜索方向,从而快速地找到实值函数的下降方向,一旦算法找到优于种群中最好点的点,利用所构造的两条直线交点的投影对其进行进一步优化,使函数值更迅速地下降.提出了适合杂交算子的初始种群生成方法.设计了一个既能提高收敛速度又能摆脱局部最优的变异算子以增强算法的效果.在此基础上,提出了一个求解全局优化问题的高效进化算法,并从理论上证明了全局收敛性,从数值上验证了有效性.  相似文献   

9.
为了求解一些非线性优化问题,采用具有脉冲出生和季节性捕杀的种群动力学模型提出了一种新的群智能优化算法(PSO-IBSK).在该算法中,假设某种群由具有幼年和成年两种阶段状态的若干个体组成,幼体是由成体脉冲产生的,经过一段时间后会变成为成体.为了提升种群的整体质量,需要季节性地对一些生长状况不良的成体进行捕杀.该算法中的出生算子和成长算子可分别实现成体向幼体瞬时和延迟传递信息,有助于搜索跳出局部最优解陷阱;捕杀算子可周期性地将不良成体清除,死亡算子可将虚弱个体随机清除,该两个算子有利于提升算法的求精能力;强势算子可实现强壮个体向虚弱个体扩散强壮信息,竞争算子可实现幼年和成体之间的有效信息交换,该两个算子有利于提升算法的探索能力;进化算子可确保算法具有全局收敛性.该算法的大部分参数采用该种群动力学模型确定,具有很好的科学性;该算法每次只处理个体特征数的6‰~8%,从而使时间复杂度大幅降低.测试结果表明,该算法具有较优越的性能,适于求解维数较高的优化问题.  相似文献   

10.
进化算法在求解全局优化问题时易陷入局部最优且收敛速度慢. 为了解决这一问题, 设计了一个基于下降尺度函数的杂交算子, 利用下降尺度函数与种群的关系来寻找实值函数的下降方向. 为了提高非均匀变异算子在进化后期的搜索能力, 通过均衡算子的局部搜索和全局搜索能力使其在算法后期仍能跳出局部最优. 在此基础上给出了一种新的进化算法. 最后将其与9个现有的算法进行了比较, 数值实验表明新算法快速有效.  相似文献   

11.
Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.  相似文献   

12.
In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.  相似文献   

13.
Multiobjective optimization of trusses using genetic algorithms   总被引:8,自引:0,他引:8  
In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.  相似文献   

14.
Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.  相似文献   

15.
Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.  相似文献   

16.
本文介绍一种多元插值逼近和动态搜索轨迹相结合的全局优化算法.该算法大大减少了目标函数计算次数,寻优收敛速度快,算法稳定,且可获得全局极小,有效地解决了大规模非线性复杂动态系统的参数优化问题.一个具有8个控制参数的电力系统优化控制问题,采用该算法仅访问目标函数78次,便可求得最优控制器参数。  相似文献   

17.
云搜索优化算法   总被引:1,自引:1,他引:0  
本文将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,建立了一种新的智能优化算法-云搜索优化算法(CSO)。生成与移动的云可以弥漫于整个搜索空间,这使得新算法具有较强的全局搜索能力;收缩与扩张的云团在形态上会有千奇百态的变化,这使得算法具有较强的局部搜索能力;降雨后产生新的云团可以保持云团的多样性,这也是使搜索避免陷入局优的有效手段。实验表明,基于这三点建立的新算法具有优异的性能,benchmark函数最优值的计算结果以及与已有智能优化算法的比较展现了新算法精确的、稳定的全局求解能力。  相似文献   

18.
粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。  相似文献   

19.
The Internet has created a virtual upheaval in the structural features of the supply and demand chains for most businesses. New agents and marketplaces have surfaced. The potential to create value and enhance profitable opportunities has attracted both buyers and sellers to the Internet. Yet, the Internet has proven to be more complex than originally thought. With information comes complexity: the more the information in real time, the greater the difficulty in interpretation and absorption. How can the value-creating potential of the Internet still be realized, its complexity notwithstanding? This paper argues that with the emergence of innovative tools, the expectations of the Internet as a medium for enhanced profit opportunities can still be realized. Creating value on a continuing basis is central to sustaining profitable opportunities. This paper provides an overview of the value creation process in electronic networks, the emergence of the Internet as a viable business communication and collaboration medium, the proclamation by many that the future of the Internet resides in “embedded intelligence”, and the perspectives of pragmatists who point out the other facet of the Internet—its complexity. The paper then reviews some recent new tools that have emerged to address this complexity. In particular, the promise of Pricing and Revenue Optimization (PRO) and Enterprise Profit OptimizationTM (EPO) tools is discussed. The paper suggests that as buyers and sellers adopt EPO, the market will see the emergence of a truly intelligent network—a virtual network—of private and semi-public profitable communities.  相似文献   

20.
SEO技术研究   总被引:4,自引:0,他引:4  
为了利用搜索引擎优化SEO(Search Engine Optimization)技术给网站带来高质量的流量并将其转化为商业利益,理解搜索引擎的算法和排名原理十分必要。通过对网站的结构优化、关键词优化、单页优化、防止被搜索引擎惩罚和挽救被惩罚网站等技术的研究,达到提高网站排名,实现网站的价值目的。  相似文献   

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