基于Spark框架和PSO优化算法的电力通信网络安全态势预测

随着电力通信网络规模的不断扩大,电力通信网络不间断地产生海量通信数据。同时,对通信网络的攻击手段也在不断进化,给电力通信网络的安全造成极大威胁。针对以上问题,结合Spark大数据计算框架和PSO优化神经网络算法的优点,提出基于Spark内存计算框架的并行PSO优化神经网络算法对电力通信网络的安全态势进行预测。本研究首先引入Spark计算框架,Spark框架具有内存计算以及准实时处理的特点,符合电力通信大数据处理的要求。然后提出PSO优化算法对神经网络的权值进行修正,以增加神经网络的学习效率和准确性。之后结合RDD的并行特点,提出了一种并行PSO优化神经网络算法。最后通过实验比较可以看出,基于Spark框架的PSO优化神经网络算法的准确度高,且相较于传统基于Hadoop的预测方法在处理速度上有显著提高。     

多种群并行协作的粒子群算法

针对高维复杂优化问题在求解时容易产生维数灾难导致算法极易陷入局部最优的问题,提出一种能够综合考虑高维复杂优化问题的特性,动态调整进化策略的多种群并行协作的粒子群算法。该算法在分析高维复杂问题求解过程中的粒子特点的基础上,建立融合环形拓扑、全连接形拓扑和冯诺依曼拓扑结构的粒子群算法的多种群并行协作的网络模型。该模型结合3种拓扑结构的粒子群算法在解决高维复杂优化问题时的优点,设计一种基于多群落粒子广播-反馈的动态进化策略及其进化算法,实现高维复杂优化环境中拓扑的动态适应,使算法在求解高维单峰函数和多峰函数时均具有较强的搜索能力。仿真结果表明,该算法在求解高维复杂优化问题的寻优精度和收敛速度方面均有良好的性能。     

基于改进PSO算法的LSSVM入侵检测模型

在基本PSO算法和线性权重下降PSO算法的基础上,提出一种并行PSO算法,将粒子群分成两组,分别采用不同的惯性权重,各侧重于全局搜索和局部搜索,根据进化代数动态调整两种算法中进化的粒子数。通过仿真实验,证明了并行PSO算法的寻优性能优于基本PSO算法和线性权重下降PSO算法。     

动态离散粒子群优化算法

为解决现实世界中动态环境下的离散事件优化问题,研究了当前已被广泛应用于动态环境或离散运算优化问题的粒子群优化算法(PSO),据此提出了一种动态离散PSO算法.该算法设计了一种环境绝对值和环境敏感性判定策略来实现动态环境的监测与响应,并通过带变异算子的离散PSO算法公式的重新定义来满足大规模离散运算需求.最后,利用离散时间系统的零状态响应求解评价了该算法的性能,结果表明,该算法在定义域内具有较好的收敛性.     

基于BSP并行计算模型的并行微粒群算法

在对标准微粒群算法分析的基础上,将它与BSP并行计算模型相结合,设计并实现了一种基于BSP并行计算模型的并行微粒群算法.这种基于BSP并行计算模型的并行微粒群算法改变了标准微粒群算法的结构,提高了算法求解效率.实验结果表明,该并行算法的性能比标准微粒群算法有了很大的提高.     

基于文化微粒群算法的丙烯腈收率软测量模型

丙烯腈收率是丙烯腈装置的关键指标,如何得到丙烯腈收率是厂家很关注的研究,将新型优化算法用于丙烯腈收率软测量建模是1种较好的尝试。将新型微粒群优化算法用于同样新型的文化算法种群空间的优化,设计文化微粒群优化算法。它由种群空间和信念空间2部分组成,在种群空间和信念空间分别采用各自算法并行演化,同时,2个空间又根据一定的协议相互联系。分别将该算法和基本微粒群算法用于一些常用测试函数的优化问题;结果表明,与基本微粒群算法相比,文化微粒群算法加强了全局搜索能力,更容易收敛于全局最优解。最后将文化微粒群优化算法用于优化神经网络,构成文化微粒群神经网络,并将其应用于丙烯腈收率软测量建模。结果表明,此模型精度高,应用前景广阔。     

Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization

A multiple-swarm multiobjective particle swarm optimization (PSO) algorithm, named dynamic multiple swarms in multiobjective PSO, is proposed in which the number of swarms is adaptively adjusted throughout the search process via the proposed dynamic swarm strategy. The strategy allocates an appropriate number of swarms as required to support convergence and diversity criteria among the swarms. Additional novel designs include a PSO updating mechanism to better manage the communication within a swarm and among swarms and an objective space compression and expansion strategy to progressively exploit the objective space during the search process. Comparative study shows that the performance of the proposed algorithm is competitive in comparison to the selected algorithms on standard benchmark problems. In particular, when dealing with test problems with multiple local Pareto fronts, the proposed algorithm is much less computationally demanding. Sensitivity analysis indicates that the proposed algorithm is insensitive to most of the user-specified design parameters.     

多任务处理协同进化粒子群算法

粒子群算法及其改进算法专注于单任务的求解.随着电子商务的发展,在线服务器在某一时刻需要同时处理来自多个用户的服务请求,即多任务处理.区别于以往的并行计算机,文中充分挖掘粒子群算法的“隐并行性”,并引入协同进化机制,在同一搜索空间根据任务个数设置不同的子种群,各子种群以一定的概率相互传递有效信息,最后提出基于多任务处理协同进化粒子群算法(CPSOM),并将CPSOM应用于多任务连续型函数优化问题、多任务离散型属性选择问题以及多任务约束工程优化问题.仿真实验表明,在CPSOM多任务环境中,不同任务之间确实存在有效信息的传递,不同任务之间的相互协作不仅可以提高解的质量,而且可以加快各优化问题的收敛速度.     

A DE and PSO based hybrid algorithm for dynamic optimization problems

Many real world optimization problems are dynamic in which the fitness landscape is time dependent and the optima change over time. Such problems challenge traditional optimization algorithms. For such problems, optimization algorithms not only have to find the global optimum but also need to closely track its trajectory. In this paper, a new hybrid algorithm integrating a differential evolution (DE) and a particle swarm optimization (PSO) is proposed for dynamic optimization problems. Multi-population strategy is adopted to enhance the diversity and try to keep each subpopulation on a different peak in the fitness landscape. A hybrid operator combining DE and PSO is designed, in which each individual is sequentially carried out DE and PSO operations. An exclusion scheme is proposed that integrates the distance based exclusion scheme with the hill-valley function to track the adjacent peaks. The algorithm is applied to the set of benchmark functions used in CEC 2009 competition for dynamic environment. Experimental results show that it is more effective in terms of overall performance than other comparative algorithms.     

Fibonacci multi-modal optimization algorithm in noisy environment

Noises are very common in practical optimization problems. It will cause interference on optimization algorithms and thus makes the algorithms difficult to find a true global extreme point and multiple local extreme points. For the problem, this paper proposes a Fibonacci multi-modal optimization (FMO) algorithm. Firstly, the proposed algorithm alternates between global search and local optimization in order not to fall into local optimum points and to retain multiple optimum points. And then, a Fibonacci regional scaling criterion is proposed in the FMO algorithm to alleviate the effects of noise, and the position of optimum point is determined according to its probability distribution under noise interference. In experiments, we evaluate the performance of the proposed FMO algorithm through 35 benchmark functions. The experimental results show that compared with Particle Swarm Optimization (PSO) algorithm, three improved versions of PSO, and Genetic algorithm (GA), the proposed FMO algorithm can gain more accurate location of optimum point and more global and local extreme points under noisy environment. Finally, an example of practical optimization in radio spectrum monitoring is used to show the performance of the FMO algorithm.     

一种辨识Wiener-Hammerstein模型的新方法

针对非线性Wiener-Hammerstein模型,提出利用粒子群优化算法对非线性模型进行辨识的新方法.该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题;然后采用粒子群优化算法获得该优化问题的解.为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法.最后,仿真结果验证了该方法的有效性和可行性.     

Non-parametric particle swarm optimization for global optimization

In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability.     

MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

This paper presents a decentralized/peer-to-peer architecture-based parallel version of the vector evaluated particle swarm optimization (VEPSO) algorithm for multi-objective design optimization of laminated composite plates using message passing interface (MPI). The design optimization of laminated composite plates being a combinatorially explosive constrained non-linear optimization problem (CNOP), with many design variables and a vast solution space, warrants the use of non-parametric and heuristic optimization algorithms like PSO. Optimization requires minimizing both the weight and cost of these composite plates, simultaneously, which renders the problem multi-objective. Hence VEPSO, a multi-objective variant of the PSO algorithm, is used. Despite the use of such a heuristic, the application problem, being computationally intensive, suffers from long execution times due to sequential computation. Hence, a parallel version of the PSO algorithm for the problem has been developed to run on several nodes of an IBM P720 cluster. The proposed parallel algorithm, using MPI's collective communication directives, establishes a peer-to-peer relationship between the constituent parallel processes, deviating from the more common master-slave approach, in achieving reduction of computation time by factor of up to 10. Finally we show the effectiveness of the proposed parallel algorithm by comparing it with a serial implementation of VEPSO and a parallel implementation of the vector evaluated genetic algorithm (VEGA) for the same design problem.     

一种新的分阶段进化的粒子群优化算法

针对粒子群优化算法在优化多极值点复杂问题时容易陷入局部极值的不足,提出一种新的分阶段进化的粒子群优化算法。该方法进化过程分为两个阶段,每个阶段对应一个不同的模型,通过结合这两种模型的各自优点有效地降低群体陷入局部最优。仿真实验结果表明,对于复杂多极值函数优化问题,本文算法比标准粒子群算法的寻优能力更强。     

A novel particle swarm optimizer hybridized with extremal optimization

Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO–EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. We testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other meta-heuristics. The proposed approach is shown to have superior performance and great capability of preventing premature convergence across it comparing favorably with the other algorithms.     

基于文化框架的随机粒子群优化算法

提出了随机粒子群优化算法(rPSO),并将其与标准PSO纳入到文化算法(CA)框架中,建立了基于文化框架的随机粒子群优化算法(CA-rPSO)。该算法以rPSO作为信念空间的进化算法,以PSO作为群体空间的进化算法,形成了两者独立并行进化的"双演化双促进"机制。选取5个测试函数进行了仿真实验分析并与其他算法进行了比较,结果表明CA-rPSO的寻优性能得到显著提高,且算法简单、易于实现。     

A novel stability-based adaptive inertia weight for particle swarm optimization

Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of birds. The performance of the PSO algorithm highly depends on choosing appropriate parameters. Inertia weight is a parameter of this algorithm which was first proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. This paper presents an adaptive approach which determines the inertia weight in different dimensions for each particle, based on its performance and distance from its best position. Each particle will then have different roles in different dimensions of the search environment. By considering the stability condition and an adaptive inertia weight, the acceleration parameters of PSO are adaptively determined. The corresponding approach is called stability-based adaptive inertia weight (SAIW). The proposed method and some other models for adjusting the inertia weight are evaluated and compared. The efficiency of SAIW is validated on 22 static test problems, moving peaks benchmarks (MPB) and a real-world problem for a radar system design. Experimental results indicate that the proposed model greatly improves the PSO performance in terms of the solution quality as well as convergence speed in static and dynamic environments.     

求解约束优化的改进粒子群算法

针对种群初始化时粒子过于集中和基本粒子群算法搜索精度不高的缺陷,提出了一种求解约束优化问题的改进粒子群算法。该算法引入佳点集技术来优化种群的初始粒子,使种群粒子初始化时分布均匀,因而种群具有多样性,不会陷入局部极值;同时使用协同进化技术使双种群之间保持通信,从而提高算法的搜索精度。仿真实验结果表明：将该算法用于5个基准测试函数,该算法均获得了理论最优解,其中有4个函数的测试方差为0。该算法提高了计算精度且鲁棒性强,可以广泛应用于其他约束优化问题中。     

两群微粒群优化算法及其应用

针对微粒群优化算法容易陷入局部极值的缺陷,提出两群微粒群优化算法．通过对5种常用测试函数进行测试和比较,结果表明两群微粒群优化算法比基本微粒群优化算法更容易找到全局最优解,优化效率明显提高．然后将两群微粒群优化算法用于催化裂化装置主分馏塔轻柴油95%点软测量建模,通过与实际工业数据对比,表明该软测量模型具有高的精度、好的性能和广阔的应用前景．     

Swarm-based intelligent optimization approach for layout problem

Layout problem is a kind of NP-Complete problem. It is concerned more and more in recent years and arises in a variety of application fields such as the layout design of spacecraft modules, plant equipment, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are considered powerful tools for solving this kind of problems. While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In proposed approach, chaos initialization and multi-subpopulation evolution strategy based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure is adaptive with evolution process. That is to say, it can avoid early premature as well as benefit speeding up convergence of later period effectively. And more importantly, proposed PSO update operators based on different versions PSO are introduced into presented algorithm. It can take full advantage of the outstanding convergence characteristic of particle swarm optimization (PSO) and improve the global performance of the proposed algorithm. An example originated from layout of printed circuit boards (PCB) and plant equipment shows the feasibility and effectiveness of presented algorithm.