基于二阶分段式的Apriori算法优化

针对数据的复杂多样性以及Apriori算法的低效问题,提出依托Spark计算框架的二阶分段式算法优化模型.基于K-Means方法设计并行聚类分析算法,采用该算法对多维多属性值数据类型进行聚类分析,提高数据差异性,降低数据规模.通过"字典表"化存储模式压缩数据量,采用"与"操作降低I/O和去候选频繁项集优化Apriori算法,利用优化后的Apriori算法挖掘聚类后数据的关联规则.通过算法分析及实验验证,当数据量达到"拐点"时优化后的算法模型相对于原Apriori算法执行效率提高47％以上,且不受数据复杂度和噪声影响,提高了规则的形成效率和鲁棒性.     

基于MapReduce的蚁群优化算法实现方法

探讨了蚁群算法的几种并行方式与适用场景以及结合云计算编程框架MapReduce的可行性,并将局部搜索类蚁群优化算法抽象为几个组件,分别与MapReduce框架的几个接口对应实现,从而为该类蚁群优化算法在MapReduce框架下实现并行化提供了一种灵活、扩展性好的解决方案。最后通过旅行商问题的仿真实验验证了所提方法的有效性。     

改进蚁群算法的无线传感器网络路径优化

研究无线传感器网络路径优化问题,针对无线传感器网络(WSN)路径优化问题,在分析了遗传算法和蚁群算法各自优缺点的基础上,通过把蚁群算法作为WSN路径优化的主框架,采用遗传算的选择、交叉和变异算子提高蚁群算法搜索速度,提出一种改进蚁群算法的WSN路径优化方法。仿真结果表明,改进蚁群算法有效地克服了基本蚁群算法的缺陷,提高了WSN路径优化效率和成功率,减少了能理消耗,有效延长了网络生存时间。     

可求解多目标优化问题的演化博弈优化算法

借鉴演化博弈的思想和选择机制,提出了一种新的基于演化博弈的优化算法(EGOA)用于多目标问题的求解.算法框架具备对该类问题的通用性.为了对算法性能进行评估,采用了一组多目标优化问题(MOPs)的测试函数进行实验.实验结果表明,使用本算法搜索得到的演化稳定策略集合能够很好地逼近多目标优化问题的帕累托前沿,与一些经典的演化算法相比具有良好的问题求解能力.     

可求解多目标优化问题的演化博弈优化算

借鉴演化博弈的思想和选择机制,提出了一种新的基于演化博弈的优化算法(EGOA)用于多目标问题的求解.算法框架具备对该类问题的通用性.为了对算法性能进行评,采用了一组多目标优化问题(MOPs)测试函数进行实验.实验结果表明,使用本算法搜索得到的演化稳定策略集合能够很好地逼近目标优化问题的帕累托前沿,与一些经典的演化算法相比具有良好的问题求解力.     

改进的多尺度随机算法

多尺度随机模拟算法(multiscale stochastic simulation algorithm,MSSA)是模拟刚性化学反应系统的有效算法.但该算法对于慢-子系统上分子数目较大的刚性系统是低效的.为了有效地模拟该类系统,本文提出了τ-Leap算法改进的多尺度随机模拟算法(improved muhiscale stochastic simulation algorithm with τ-Leap,IMSS τ).算法将τ-Leap算法应用于IMSS算法来对其中的慢-子系统进行模拟,而舍弃了原方法中Gillespie的精确随机模拟算法(stochastic simulation algorithm,SSA),在精度损失较小的条件下,使模拟速度较IMSS算法提高了95%.数值模拟结果证实了IMSS τ的优点.     

复杂动力学模型参数优化问题的协同进化算法

复杂反应动力学建模中,系统参数的优化是需要解决的关键问题之一.该类优化问题具有多参数、非线性以及参数相关性强等特点.协同进化算法将多种群之间的协同作用以及种群内部的独立进化相结合,适合于求解该类问题.将改进的协同进化算法应用到化工氧化反应建模过程的系统参数优化问题中,避免了解决该类问题的传统优化算法中易陷入局部极值以及初值依赖性强的缺点,运用理论证明了该算法的有效性.测试结果表明,协同进化算法对于求解该类复杂参数优化问题是有效的.     

基于混合算法的知识网运算表达式优化

根据知识化制造的相关概念,为了实现基于用户功能需求的知识网自动生成,研究了知识网多重集运算表达式的优化问题,给出该问题的优化模型,并采用遗传-禁忌搜索混合算法进行求解.在该混合算法中,遗传算法提供并行搜索的主框架,禁忌搜索作为遗传算法的变异算子.通过与遗传算法进行比较,得出该算法有更高的计算效率,对求解该类问题有着很好的效果.     

基于QoS控制的连续媒体服务任务调度

连续媒体服务如视频,音频等是一类新的实时应用,要求在一统一的操作系统框架内支持强,弱实时应用,而传统的操作系统中的调度策略不能很好的支持该类应用。文中提出了一种新的ＱｏＳ描述方法,并在此基础上提出支持连续媒体流的,基于ＱｏＳ的启发式任务调度算法。该算法考虑了多媒体任务的成功率和连续失败数在资源有限的前提下,尽量保证所有媒体流的服务质量。     

改进的Great Deluge算法求解流水车间调度

Great deluge algorithm（GDA）是由Threshold accepting algorithm（TAA）演变而来的一种新的巨集启发式算法,它的实现只需要一个参数的设定。目前,GDA在车间调度优化方面的应用还很少,文中对其改进后将其应用于解决流水车间调度问题,并通过实例仿真对其优化效果进行了评价。文中先将算法按原有形式实现,但优化效果不佳;后对算法提出改进策略：即将算法中唯一参数的值设为与优化过程中出现的一个差值成正比例变化（原算法中设为一个定值）,并在此基础上对算法加入最优方案保存策略,实例的仿真结果表明,这一改进有效地克服了原算法求解该问题时出现的＂过早收敛＂现象,大大提高了算法的全局满意度,对解决该类问题有很好的效果,而在加入最优方案保存策略后,算法对该问题的优化效果得到进一步提高。     

和声搜索算法优化多时间窗多式联运运输方案

针对多式联运运输路径上运输方式选择问题,考虑运输网络中多个节点存在服务时间窗的限制,建立了多个中间节点带软时间窗的多式联运运输方案优化模型,设计了一种基于字符编码方式的和声搜索算法,该算法采用新的和声生成方式及微调方式。仿真实例表明,所提算法与贪婪算法相比能获得具有更优运输总成本及不准点时间的运输方案。     

Multimodal function optimization using minimal representation size clustering and its application to planning multipaths

A novel genetic algorithm (GA) using minimal representation size cluster (MRSC) analysis is designed and implemented for solving multimodal function optimization problems. The problem of multimodal function optimization is framed within a hypothesize-and-test paradigm using minimal representation size (minimal complexity) for species formation and a GA. A multiple-population GA is developed to identify different species. The number of populations, thus the number of different species, is determined by the minimal representation size criterion. Therefore, the proposed algorithm reveals the unknown structure of the multimodal function when a priori knowledge about the function is unknown. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The proposed scheme results in a highly parallel algorithm for finding multiple local minima. In this paper, a path-planning algorithm is also developed based on the MRSC_GA algorithm. The algorithm utilizes MRSC_GA for planning paths for mobile robots, piano-mover problems, and N-link manipulators. The MRSC_GA is used for generating multipaths to provide alternative solutions to the path-planning problem. The generation of alternative solutions is especially important for planning paths in dynamic environments. A novel iterative multiresolution path representation is used as a basis for the GA coding. The effectiveness of the algorithm is demonstrated on a number of two-dimensional path-planning problems.     

Artificial bee colony algorithm and pattern search hybridized for global optimization

Artificial bee colony algorithm is one of the most recently proposed swarm intelligence based optimization algorithm. A memetic algorithm which combines Hooke–Jeeves pattern search with artificial bee colony algorithm is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the exploration phase realized by artificial bee colony algorithm and the exploitation phase completed by pattern search. The proposed algorithm was tested on a comprehensive set of benchmark functions, encompassing a wide range of dimensionality. Results show that the new algorithm is promising in terms of convergence speed, solution accuracy and success rate. The performance of artificial bee colony algorithm is much improved by introducing a pattern search method, especially in handling functions having narrow curving valley, functions with high eccentric ellipse and some complex multimodal functions.     

分数阶策略和带有Lévy飞行的螺旋蝙蝠算法

针对蝙蝠算法在求解多峰、复杂非线性问题时,搜索效率降低、易陷入局部最优等不足,提出了一种改进的蝙蝠算法。引入具有短期记忆特性的分数阶策略来更新蝙蝠位置,增加种群多样性,提高了算法收敛速度;用带有Lévy飞行的阿基米德螺旋策略产生局部新解,增强局部开发能力,同时有助于算法跳出局部最优;采用新的非线性动态机制调节响度和脉冲发射率,以平衡算法的探索和开发。选取CEC2014测试集,包括单峰、多峰、混合以及复合函数,对提出的算法和其他群智能算法进行仿真实验,测试结果表明提出的算法搜索效率和求解精度相较于对比算法得到提升,用Friedman统计分析验证了算法的优越性。将提出的算法用于求解机械工程减速器设计问题,与PSO-DE、WCA、APSO进行实验对比,验证该算法的有效性。     

An improved Simulated Annealing algorithm based on ancient metallurgy techniques

Simulated Annealing (SA) is a single-solution-based metaheuristic technique based on the annealing process in metallurgy. It is also one of the best-known metaheuristic algorithms due to its simplicity and good performance. Despite its interesting characteristics, SA suffers from several limitations such as premature convergence. On the other hand, Japanese swordsmithing refers to the manual-intensive process for producing high-quality bladed weapons from impure raw metals. During this process, Japanese smiths fold and reheat pieces of metal multiple times in order to eliminate impurities and defects. In this paper, an improved version of the SA algorithm is presented. In the new approach, a population of agents is considered. Each agent conducts a search strategy based on a modification of the SA scheme. The proposed algorithm modifies the original SA incorporating two new operators, folding and reheating, inspired by the ancient Japanese Swordsmithing technique. Under the new approach, the process of folding is conceived as a compression of the search space, while the reheating mechanism considers a reinitialization of the cooling process in the original SA scheme. With this inclusion, the new algorithm maintains the computational structure of the SA method but improving its search capacities. In order to evaluate its performance, the proposed algorithm is tested in a set of 28 benchmark functions, which include multimodal, unimodal, composite and shifted functions, and 3 real world optimization problems. The results demonstrate the high performance of the proposed method when compared to the original SA and other popular state-of-the-art algorithms.     

多元多峰值目标函数在MATLAB中的优化

采用MATLAB的遗传算法,利用强大的数学计算能力和遗传工具箱,在全局搜索空间内寻找极值点,能够有效地对多元多峰值函数进行优化,避免了利用传统优化方法在多元多峰值函数优化过程中陷入局部极值点的优化误区。同时简要介绍了遗传算法的特点、流程和优化工具箱,通过实际编程优化,说明基于MATLAB的遗传算法是一种具有良好的全局寻优的优化工具。     

基于小生境的混合差分演化模拟退火算法

提出了一种新的演化算法——基于小生境的混合差分演化-模拟退火算法(NDESA算法),分析了构造NDESA算法的合理性。并且结合典型多峰值测试函数——Shubert函数的求解试验,说明NDESA算法能够高效地、快速地找到具有多个全局最优值点的多峰函数的所有全局最优值点,且参数的选择不必很严格,是一种较好地求解多峰值函数的所有最优值点的方法。还通过实验说明了结合小生境,差分演化和模拟退火算法这三种策略的必要性。     

A local multiobjective optimization algorithm using neighborhood field

A new local search algorithm for multiobjective optimization problems is proposed to find the global optima accurately and diversely. This paper models the cooperatively local search as a potential field, which is called neighborhood field model (NFM). Using NFM, a new Multiobjective Neighborhood Field Optimization (MONFO) algorithm is proposed. In MONFO, the neighborhood field can drive each individual moving towards the superior neighbor and away from the inferior neighbor. MONFO is compared with other popular multiobjective algorithms under twelve test functions. Intensive simulations show that MONFO is able to deliver promising results in the respects of accuracy and diversity, especially for multimodal problems.     

Feedback image encryption algorithm with compound chaotic stream cipher based on perturbation

In this paper, two new one-dimensional chaotic functions are designed using Devaney chaotic definition. And a dynamically shifting compound chaotic function is constructed based on the two new one-dimensional chaotic functions. The properties of compound chaotic functions are also proved. A new feedback image encryption algorithm is designed using the new compound chaos and an image pixel permutation, 3D baker scheme is described in detail. In the scheme, a new dynamic block dividing the 3D baker is put for...     

基于余弦控制因子和多项式变异的鲸鱼优化算法

针对基本鲸鱼优化算法(Whale optimization algorithm,WOA)在求解最优解不在原点附近的目标函数时存在收敛精度低、易陷入局部最优解的缺陷,提出一种基于余弦控制因子和多项式变异的鲸鱼优化算法(CPWOA).所提算法中控制参数按照余弦曲线变化,并加入同步余弦惯性权值,使得在迭代前期减缓收敛速度以进行充分的全局探索,而在迭代后期加速收敛以提高算法精度;同时,对最佳鲸鱼位置引入多项式变异,以增强算法跳出局部最优解的能力.将所提算法对多个shifted单峰、多峰和固定维测试函数进行求解,实验结果表明,与基本WOA、EHO、GWO、SCA、MBO以及其他改进型WOA算法相比,CPWOA对绝大多数测试函数的求解有更高的精度和稳定性.用非参数估计方法对计算结果进行差异显著性统计检验,表明CPWOA算法的显著性更优.