改进混沌PSO算法的电力系统最优潮流计算

电力工业的市场化改革对最优潮流（Optimal Power Flow,OPF）的计算精度和速度提出了更高的要求。在分析最优潮流理论及其算法的基础上,对比一些经典解算法,引入粒子群优化算法（PSO）来计算发电厂成本耗费问题。考虑到传统PSO算法处理OPF约束条件时,对随机粒子个体的质量和速度的选取不能保证,且收敛速度慢,并容易陷入局部最优解,提出改进的混沌粒子群算法,即利用混沌运动特性来改进粒子群算法。利用该算法与其他算法对IEEE5节点算例进行分析比较,结果表明改进的混沌微粒群优化算法可较好处理最优潮流约束条件,有效提高了PSO算法的全局收敛能力和计算精度。在处理最优潮流问题上具有一定的有效性和优越性。     

教与学信息交互粒子群优化算法

针对单一种群在解决高维问题中收敛速度较慢和多样性缺失的问题,提出了一种教与学信息交互粒子群优化（PSO）算法。根据进化过程将种群动态地划分为两个子种群,分别采用粒子群优化算法和教与学优化算法,同时粒子利用学习者阶段进行子种群之间信息交互,并通过评价收敛性和多样性指标让粒子的收敛能力和多样性在进化过程中得到平衡。与粒子群优化算法、混合灰狼粒子群算法、重选精英个体的非线性收敛灰狼优化（GWO）算法等多个进化算法在15个标准测试函数的不同维度下进行对比实验,所提算法在多个测试函数上可以收敛到理论最优值,速度相对于其他算法提高了1~6倍。实验结果表明,所提算法在收敛精度和收敛速度上具有较好的效果。     

结合SCE法的粒子群优化QoS路由算法

QoS(QualityofService)路由问题是一个非线性的组合优化问题,理论上已证明了该问题是NP完全问题。粒子群优化算法是一种基于群智能演化计算技术,PSO在求解连续性优化问题上得到了较好的应用,而把PSO算法用于求解路由算法等离散性问题还比较少见,同时,PSO算法在收敛过程中还存在随机性,某些情况下会出现停滞现象。为此本文提出了一种结合SCE(shuffledcomplexevolution)法的粒子群优化方法用于求解QoS路由问题。该算法通过引入插入算子,删除算子,算子系列和基本算子序列等概念,对基本的粒子群优化算法进行改进;通过采用SCE法,使算法跳出局部最优解的限制。仿真结果显示,该算法取得了满意的效果,在寻优速度上优于遗传算法,也提高了算法收敛到最优解的能力。     

基于分层自主学习的改进粒子群优化算法

针对粒子群优化（PSO）算法容易陷入局部最优、收敛精度不高、收敛速度较慢的问题，提出一种基于分层自主学习的改进粒子群优化（HCPSO）算法。首先，根据粒子适应度值和迭代次数将种群动态地划分为三个不同阶层；然后，根据不同阶层粒子特性，分别采用局部学习模型、标准学习模型以及全局学习模型，增加粒子多样性，反映出个体差异的认知对算法性能的影响，提高算法的收敛速度和收敛精度；最后，将HCPSO算法与PSO算法、自适应多子群粒子群优化（PSO-SMS）算法以及动态多子群粒子群优化（DMS-PSO）算法分别在6个典型的测试函数上进行对比仿真实验。仿真结果表明，HCPSO算法的收敛速度和收敛精度相对给出的对比算法均有明显提升，并且算法执行时间和基本PSO算法执行时间差距在0.001量级内，在不增加算法复杂度的情况下算法性能更高。     

基于动态压力控制算子的磷虾群算法

针对基础磷虾群（KH）算法在求解复杂函数优化问题时局部搜索能力差、求解精度低、收敛速度慢、容易陷入局部最优等问题,提出一种基于动态压力控制算子的磷虾群算法（DPCKH）。该算法将一种新的动态压力控制算子加入了标准磷虾群算法,使其处理复杂函数优化问题更有效。动态压力控制算子通过欧氏距离量化了多个不同优秀个体对目标个体的诱导效应,进而在优秀个体附近加速产生新磷虾个体,提高了磷虾个体的局部探索能力。通过比较蚁群算法（ACO）、差分进化算法（DE）、磷虾群算法（KH）、改进的磷虾群算法（KHLD）和粒子群算法（PSO）,DPCKH算法在7个测试函数上的结果表明,DPCKH算法与ACO算法、DE算法、KH算法、KHLD算法和PSO算法相比有着更强的局部勘测能力,其开采能力更强。     

求解TSP问题的模糊自适应粒子群算法

由于惯性权值的设置对粒子群优化（PSO）算法性能起着关键的作用，本文通过引入模糊技术，给出了一种惯性权值的模糊自适应调整模型及其相应的粒子群优化算法，并用于求解旅行商（TSP）问题。实验结果表明了改进算法在求解组合优化问题中的有效性，同时提高了算法的性能，并具有更快的收敛速度。     

求解优化问题的混合PSO-Solver算法

融合了粒子群算法(PSO) 和Solver 加载宏,形成混合PSO-Solver算法进行优化问题的求解。PSO作为全局搜索算法首先给出问题的全局可行解,Solver则是基于梯度信息的局部搜索工具,对粒子群算法得出的解再进行改进,二者互相结合,既加快了全局搜索的速度,又有效地避免了陷入局部最优。算法用VBA语言进行编程,简单且易于实现。通过对无约束优化问题和约束优化问题的求解,以及和标准PSO、其他一些混合算法的比较表明,PSO-Solver算法能够有效地提高求解过程的收敛速度和解的精确性。     

基于群智能的连续优化算法研究

在对蚁群优化算法(ACO)和粒子群优化算法(PSO)进行分析的基础上,提出一种解决函数连续优化的群智能混合策略-CA-PSO.在求解过程中,首先对解空间进行区域划分,进而利用ACO在优化初期具备的快速收敛性能,在整个解空间内搜索最优解的敏感区域.然后利用蚁群的搜索结果初始化PSO粒子,利用PSO快速和全局收敛性进行所在小区域内的搜索.种群更新时根据蚁群的拓扑结构和小区域间的阶跃规则,蚁群不断向最优解敏感区域聚集,使得敏感区域内粒子数增加,则局部的PSO搜索策略可以更细密的搜索最优.实例结果表明,CA-PSO既能保证解的分布性与多样性,又避免了在多峰值函数寻优过程中陷入局部最优解而停止运算,最终将收敛到全局最优解.     

基于TanW-ArccosC PSO算法的PP求解与实证分析

通过结合正切函数Tan-W和反余弦函数Arccos-C提出了一种改进的粒子群优化算法,简称TanW-ArccosC PSO算法。TanW-ArccosC PSO算法通过对惯性权重和学习因子的改进,增加了粒子群的多样性,增强了算法的搜索能力,提高了算法的收敛速度。针对投资组合问题,通过在大智慧软件中随机提取数据,利用MATLAB软件,分别用改进的TanW-ArccosC PSO算法和标准PSO算法进行求解与实证分析其投资组合问题的投资比例和CVaR值,实证分析结果表明TanW-ArccosC PSO算法具有更良好的搜索能力、低风险性以及可操作性。     

基于PSO信息传递模式的混合算法

粒子群优化算法（Particle Swarm Optimization，PSO）是一种基于群智能（Swarm Intelligence）的随机优化计算技术。PSO和遗传算法这两种算法相比较，PSO收敛快速准确，但编码形式单一，局限于解决实优化问题，而遗传算法编码形式灵活，解决问题广泛，但执行效率低于PS00。将粒子群算法的信息传递模式与遗传算法的编码和遗传操作相结合，提出一种混合算法。并推导了两个算法之间的密切联系。并通过组合优化和函数优化的基准测试集对算法进行测试，试验结果表明，该算法在收敛精度和速度优于传统遗传算法。同时，也观察到该算法取得了与粒子群算法一致的收敛现象。     

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

ABSTRACT

A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimisation domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimisation (PSO), Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-Adaptive Differential Evolution Algorithm, Comprehensive Learning Particle Swarm Optimisation, Backtracking Search Optimisation Algorithm, and Ideology Algorithm. The Wilcoxon signed-rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations is discussed as well. In addition, an illustrative example is also solved and every detail is provided.     

Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.     

基于粒子群优化的仓虫分类识别技术研究

在对仓虫分类识别过程中,为了改善因采用BP神经网络产生的由于训练时间长和易于陷入局部极小点,而导致效率和分类的准确性较低的情况,对粒子群优化算法进行了研究,并把这种算法运用到神经网络学习训练中。实验表明,将基于粒子群优化的神经网络算法应用到仓虫分类中,从训练时间、识别率上得到了较大的改善,而且算法易于实现,且能更快地收敛于全局最优解。     

小生境粒子群优化算法

针对粒子群算法容易早熟收敛和后期收敛速度慢的缺点,结合进化论中小生境技术,提出了小生境粒子群优化算法。通过粒子之间的距离找到具有相似距离的粒子个体组成小生境种群,然后在该种群里面利用粒子群优化算法进化粒子,所有个体经过其小生境群体的进化之后,找到最优的个体存入到下一代的粒子群中,直到找到满意的适应值为止。最后利用Shaffer函数验证了该算法的性能,并且与其他算法进行比较,结果表明该文算法能获得比较好的解,收敛成功率高,并且代价也比较小。     

Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform

The introduction of NVidia’s powerful Tesla GPU hardware and Compute Unified Device Architecture (CUDA) platform enable many-core parallel programming. As a result, existing algorithms implemented on a GPU can run many times faster than on modern CPUs. Relatively little research has been done so far on GPU implementations of discrete optimisation algorithms. In this paper, two approaches to parallel GPU evaluation of the Permutation Flowshop Scheduling Problem, with makespan and total flowtime criteria, are proposed. These methods can be employed in most population-based algorithms, e.g. genetic algorithms, Ant Colony Optimisation, Particle Swarm Optimisation, and Tabu Search. Extensive computational experiments, on Tabu Search for Flowshop with both criteria, followed by statistical analysis, confirm great computational capabilities of GPU hardware. A GPU implementation of Tabu Search runs up to 89 times faster than its CPU counterpart.     

Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems

Particle swarm optimisation (PSO) is a well-established optimisation algorithm inspired from flocking behaviour of birds. The big problem in PSO is that it suffers from premature convergence, that is, in complex optimisation problems, it may easily get trapped in local optima. In this paper, a new PSO variant, named as enhanced leader PSO (ELPSO), is proposed for mitigating premature convergence problem. ELPSO is mainly based on a five-staged successive mutation strategy which is applied to swarm leader at each iteration. The experimental results confirm that in all terms of accuracy, scalability and convergence rate, ELPSO performs well.     

基于粒子群优化和禁忌搜索的混合调度算法

提出了用于解决作业车间调度问题的离散版粒子群优化算法。该算法采用基于先后表编码方案和新的位移更新模型,使具有连续本质的粒子群优化算法直接适用于车间调度问题。同时,利用粒子群优化算法的全局搜索能力和禁忌搜索算法的自适应优点,将粒子群优化算法和禁忌搜索结合起来,设计了广义粒子群优化算法和粒子群—禁忌搜索交替算法两种混合调度算法。实验结果表明,两种混合调度算法能够有效地、高质量地解决作业车间调度问题。     

Selective disassembly planning for waste electrical and electronic equipment with case studies on liquid crystaldisplays

Waste Electrical and Electronic Equipments (WEEEs) are one of the most significant waste streams in modern societies. In the past decade, disassembly of WEEE to support remanufacturing and recycling has been growingly adopted by industries. With the increasing customisation and diversity of Electrical and Electronic Equipment (EEE) and more complex assembly processes, full disassembly of WEEE is rarely an ideal solution due to high disassembly cost. Selective disassembly, which prioritises operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important but still a challenging research topic in recent years. In order to address the issue effectively, in this paper, a Particle Swarm Optimisation (PSO)-based selective disassembly planning method embedded with customisable decision making models and a novel generic constraint handling algorithm has been developed. With multi-criteria and adaptive decision making models, the developed method is flexible to handle WEEE to meet the various requirements of stakeholders. Based on the generic constraint handling and intelligent optimisation algorithms, the developed research is capable to process complex constraints and achieve optimised selective disassembly plans. Industrial cases on Liquid Crystal Display (LCD) televisions have been used to verify and demonstrate the effectiveness and robustness of the research in different application scenarios.     

Design of water distribution networks using accelerated momentum particle swarm optimisation technique

Optimisation of looped water distribution networks (WDNs) has been recognised as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimisation techniques. This article proposes the use of a new version of heuristic particle swarm optimisation (PSO) for solving this problem. In order to increase the convergence speed of the original PSO algorithm, some accelerated parameters are introduced to the velocity update equation. Furthermore, momentum parts are added to the PSO position updating formula to get away from trapping in local optimums. The new version of the PSO algorithm is called accelerated momentum particle swarm optimisation (AMPSO). The proposed AMPSO is then applied to solve WDN design problems. Some illustrative and comparative illustrative examples are presented to show the efficiency of the introduced AMPSO compared with some other heuristic algorithms.     

基于PSO和变异模拟退火的QoS单播路由算法

为了研发更高性能的QoS单播路由算法,提出变异退火粒子群优化（MSAPSO）算法。MSAPSO算法中使用一种新的。算子,将粒子群优化（PSO）的迭代公式简化成一个公式。通过设计变异退火算子,将遗传算法的变异操作和模拟退火的Meuopofis概率接受准则融入PSO,以改善粒子群的多样性和算法的收敛性。仿真结果表明MSAPSO在搜索成功率和收敛性上优于纯PSO算法和蚁群算法。