强社会认知能力的粒子群优化算法

针对粒子群优化算法的"早熟"问题,提出了强社会认知能力粒子群优化算法,该算法通过学习概率和选择概率确定粒子跟踪的局部极值。算法中学习概率的自适应调整有效权衡了粒子的个体认知能力和社会认知能力。通过经典函数的测试结果表明,新算法的全局搜索能力有了显著提高,并且能够有效避免早熟问题。     

自适应种群更新策略的多目标粒子群算法

针对粒子种群较差的局部搜索能力,提出了一种自适应种群更新策略的多目标粒子群算法。该算法在每次种群进行迭代时,根据种群的多样性测度以及每个粒子的适应度值,自适应地改变速度权重,以此来提高种群粒子在局部搜索时的活性,使算法具有较强的局部搜索能力同时又保留了足够的全局搜索能力。最后利用多组经典测试样例进行仿真,并与传统的粒子群算法以及速度线性衰减算法做比较,在单目标优化中,自适应粒子群算法能够更快地寻找最优位置;在多目标优化中,自适应粒子群算法能够更快速地收敛于帕累托最优边界。     

一种求解Job-Shop调度问题的混合自适应变异粒子群算法

本文提出了用于解决车间作业调度问题的混合自适应变异粒子群算法,该算法在运行的过程中根据群体适应度方差以及当前最优解的大小来确定当前最佳粒子的变异概率,利用遗传算法思想对粒子进行选择、交叉操作,并将模拟退火算法的优点融入到AMPSO算法中。仿真结果表明,混合AMPSO算法能够有效地、高质量地解决作业车间调度问题。     

层次学习骨干粒子群优化算法

对骨干粒子群优化(BPSO) 种群多样性迅速丧失的原因进行分析, 提出层次学习骨干粒子群优化算法以克 服早熟现象. 该算法中粒子依不同的学习概率向粒子自身的最优粒子、优胜粒子和群体最优粒子学习, 该机制使群 体实现不同层次的搜索并有效维持群体的多样性. 此外, 群体最优粒子依概率采用跳跃策略以增强逃逸能力或采用 扰动策略以提高解的质量. 将所提出的算法与多种改进的粒子群优化算法进行对比, 仿真结果表明, 所提出算法的综 合表现优于其他算法.     

自适应变异的粒子群优化算法

针对粒子群算法的早熟收敛问题,提出一种新的基于群体适应度变化率自适应变异的粒子群优化算法。该算法根据群体适应度变化率自适应调整惯性权重的取值,根据当前种群的平均粒距对种群中部分粒子进行变异操作。自适应调整与变异操作能增强算法跳出局部最优的能力,增大寻找全局最优的几率。对几种典型函数的测试结果表明,新算法的全局搜索能力有了明显的提高,有效避免了早熟收敛问题。     

带自适应变异的量子粒子群优化算法

提出了一种带有自适应变异的量子粒子群优化(AMQPSO)算法,利用粒子群的适应度方差和空间位置聚集度来发现粒子群陷入局部寻优时,对当前每个粒子经历过的最好位置进行自适应变异以实现全局寻优。通过对典型函数的测试以及与量子粒子群优化(QPSO)算法和自适应粒子群优化(AMPSO)算法的比较,说明AMQPSO算法增强了全局搜索的性能,优于其他算法。     

改进的自适应粒子群优化算法

提出了一种融合梯度搜索法、繁殖法并结合前N 个粒子历史最优位置的改进自适应粒子群优化算法。算法选用混沌惯性权重,每个粒子速度和位置的更新不仅考虑自身历史最优和全局最优位置,还受其他粒子历史最优位置的影响,且其影响程度的权重随迭代次数自适应变化;同时粒子位置随迭代次数以线性递增的概率进行负梯度方向更新;当粒子更新停滞时,对可能处于局部最优位置的部分粒子进行杂交。仿真实验结果表明,该算法比其他相关算法具有更好的收敛速度和收敛精度。     

基于自适应粒子群优化的新型粒子滤波在目标跟踪中的应用

针对基于粒子群优化的粒子滤波(PSO-PF)算法精度不高,实时性差,难以满足雷达机动目标跟踪的需求,提出一种基于动态邻域自适应粒子群优化的粒子滤波(DPSO-PF)算法.该算法可以动态调整粒子邻域环境,其中每个粒子按照邻域的环境和自身的位置信息自适应地调整相互间的邻域粒子数量,使邻域粒子数量更为合理,达到寻优能力与收敛速度的最佳平衡.最后利用不同模型对该算法进行了仿真实验,实验结果表明所提出的算法能够提高雷达机动目标跟踪的实时性和精确性.     

基于改进粒子群算法的解耦控制研究与仿真

针对标准粒子群算法优化多变量系统的解耦控制时存在陷入非全局最优值的问题,设计出一种基于自适应变异的粒子群算法优化多变量系统解耦控制器的方法.该方法以标准粒子群算法为基础,在种群进化过程中引入变异操作,对不同进化程度的粒子以不同的概率进行更新,其中没有达到个体最优的粒子以随机的大概率进行位置与速度的初始化,对已经早熟的粒子以一定的小概率更新进化路径,以此来提高种群搜索全局最小值的能力.种群寻到最优值的状态就是控制器效果最好的状态,利用新的网络模型即可解决上述问题.经过对比仿真,验证了该方法的可行性.     

自适应动态重组多目标粒子群优化算法

提出一种自适应动态重组粒子群优化算法. 该算法采用凝聚的层次聚类算法, 将种群分成若干个子群体, 用一个精英集对非支配解进行存储; 根据贡献度和多样性, 对各子群体的粒子和整个种群进行自适应动态重组; 同时引入扰动算子对精英集存储的非支配解进行扰动, 实现对精英集进行动态调整. 利用具有不同特点的测试函数进行验证并与同类算法相比较, 结果表明, 所提出的算法可加快收敛速度, 提高种群的可进化能力.

     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

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.     

Real-time dynamic optimization of batch systems

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.     

Multiobjective optimization of trusses using genetic algorithms

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.     

Effects of dominant wolves in grey wolf optimization algorithm

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.     

An integrated approach to topology,sizing, and shape optimization

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.     

多元插值全局优化算法及其应用

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。     

云搜索优化算法

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。     

粒子群算法在组合优化问题上的研究与发展

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。     

Value Creation in a Network: The Role of Pricing and Revenue Optimization and Enterprise Profit OptimizationTM

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 Optimization^TM (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.     

SEO技术研究

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。