双层规划的改进混合布谷鸟搜索量子行为粒子群优化算法

为解决粒子群优化（PSO）算法求解双层规划问题时易陷入局部最优解的问题,提出了一种基于模拟退火（SA）Metropolis准则的改进混合布谷鸟搜索量子行为粒子群优化（ICSQPSO）算法。首先,该混合算法引入SA算法中的Metropolis准则,在求解过程中既能接受好解也能以一定的概率接受坏解,增强全局寻优能力;接着,为布谷鸟搜索算法设计一种改进动态步长Lévy飞行,以保持粒子群在优化过程中较高的多样性,保证搜索广度;最后,利用布谷鸟搜索算法中的偏好随机游走机制帮助粒子跳出局部最优解。通过对13个涵盖非线性规划、分式规划、多个下层规划的双层规划实例的数值实验,结果表明：ICSQPSO算法所得12个双层规划的目标函数最优值显著优于对比算法,只有1例的结果稍差,并且有半数实例的结果优于对比算法50%。由此可见,ICSQPSO算法对双层规划的寻优能力明显优于对比算法。     

量子粒子群优化算法在双层规划问题中的应用

双层规划问题大多都是针对某些特定问题的,采用传统的方法求解非常困难,论文采用量子粒子群优化算法来求解双层规划问题,提出了求解双层规划问题的一种通用的有效算法.最后,通过实例分析与其他算法比较,验证了本算法的有效性.     

考虑备灾的双层规划应急资源调度选址—路径优化模型与算法

备灾措施可以为救灾做准备,为确保灾后应急物资可以及时高效地到达灾区,提出了考虑备灾的双层规划应急资源调度选址—路径优化模型,上层规划以供应站建设和运营总成本最低为目标,而下层规划以配送路径成本最小化为目标.设计了一种改进的双层樽海鞘遗传算法求解该问题,结合迭代划分的概念更新领导者位置,采用自然指数惯性权值策略修正控制因子,利用混沌映射更新追随者位置,采用田口分析方法获取参数合理取值.最后,通过使用双层樽海鞘遗传算法与遗传粒子群混合算法、粒子群优化算法、免疫优化算法对OR-Library中的LRP(location-routing problem,LRP)数据集进行求解和对比分析,验证了所提模型和算法的可行性和有效性.     

基于DTC-MOPSO算法的焊接机器人路径规划

点焊机器人在汽车白车身焊接中的应用大大提高了企业的生产效率,本文从焊接路径长度和能量两方面进行焊接机器人多目标路径规划．为了很好地解决这个问题,本文对一种新型多目标粒子群算法（三态协调搜索多目标粒子群优化算法）进行改进,得到适合于求解离散多目标优化问题的离散化三态协调搜索多目标粒子群算法（DTC-MOPSO）．通过和两个经典的优化算法比较,DTC-MOPSO算法在分散性和收敛性方面都有很好的优化性能．最后运用Matlab机器人工具箱对机器人的运动学、逆运动学以及逆动力学进行分析以求解机器人的路径长度和能耗,并将改进的算法应用于焊接机器人路径规划中,结果显示规划后的路径明显优于另外两种算法.     

基于适应度值优劣粒子群算法的无人机路径规划

粒子群优化算法(PSO)是一种典型的基于群体合作的搜索算法,针对传统粒子群优化算法在求解无人机路径规划问题时存在陷入局部最优和容易早熟收敛等问题,提出基于适应度值优劣粒子群算法的路径规划方法。改进的粒子群算法将避障和路径长度作为约束条件来建立目标函数,通过将整个种群中的粒子按照适应度值划分为两个部分,并且分别设计了划分后的两个部分的惯性权重,以此来提高算法的多样性和收敛性,从而进一步提高粒子群算法的寻优能力。通过仿真实验表明：与原粒子群算法相比,根据适应度值来调整算法惯性权重能够有效提高粒子群算法的多样性和收敛性。     

求解非线性约束问题的混合粒子群优化算法

将处理约束问题的乘子法与改进的粒子群算法相结合,提出了一种求解非线性约束问题的混合粒子群算法。此算法兼顾了粒子群优化算法和乘子法的优点,对迭代过程中出现的不可行粒子,利用乘子法处理后产生可行粒子,然后用改进的粒子群算法来搜索其最优解,这样不仅减小了粒子群算法在寻优过程中陷入局部极小的概率,而且提高了搜索精度。数值试验结果表明提出的新算法具有搜索精度更高、稳定性更强、鲁棒性更好等特点。     

求解双层规划优化问题的层次风驱动优化算法

针对双层规划的求解问题, 提出一种层次风驱动优化算法. 初始化上层优化变量后, 首先对下层规划进行求解, 满足约束条件的同时, 更新下层规划中的空气质点速度和位置; 然后, 利用风驱动优化算法对上层规划问题进行求解; 最后, 在优化解集合中, 选择上下层规划目标值次序之和最小的解作为最终优化解. 实验结果表明, 所提出的层次风驱动算法是一种有效的求解双层规划问题的方法.

     

基于层次遗传算法的物流配送中心选址策略*

在考虑物流规划部门和客户双方利益的基础上,采用双层规划模型描述物流配送中心的选址问题。基于进化博弈与多目标优化的思想设计了层次遗传算法来求解该模型,通过两个遗传算法的交互迭代求解物流配送中心选址问题。最后通过算例验证了模型与算法的可行性。     

离散杂草优化算法在0/1背包问题中的应用

为解决粒子群优化算法在求解0/1背包问题中的早熟收敛问题,将杂草优化算法应用到离散问题,提出了一种离散杂草优化算法(DIWO)。根据组合优化问题的特点,对原算法中正态分布于父代周围的子代进行离散化分析,引入遗传操作中的一种改进的变异机制,保证了新算法的有效性,使其具有局部的随机搜索能力。通过三个仿真实例验证,对比粒子群算法,新算法在种群数量较小、迭代次数较少的情况下能取得更好的结果。     

改进粒子群优化算法的移动机器人路径规划

针对单一智能优化算法求解机器人路径规划时易陷入局部误区的问题,提出改进粒子群优化算法(GB_PSO)用于机器人路径规划.该算法以粒子群优化算法(particle swarm optimization,PSO)为主体,由于遗传算法(genetic algorithm,GA)和细菌觅食算法(bacterial foraging optimization algorithm,BFO)更新策略所受环境影响的不同,拟合两种环境参数;然后计算粒子与不同环境参数之间的相关性将粒子群划分为两类,分别通过GA的选择、交叉、变异算子和BFO的趋化操作并行加强局部优化;最后通过改进的粒子群更新公式对粒子进行更新,实现机器人全局和局部路径的优化.实验结果表明,改进粒子群优化算法进行路径规划提高了局部和整体的搜索能力,路径规划速度快且路径距离短,同时具备更强的鲁棒性.     

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.     

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.     

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.     

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。