基于模糊树突状细胞算法的研究

利用模糊集合论的理论来改变传统的树突状细胞算法中对半成熟树突状细胞和成熟树突状细胞的清晰化划分问题。传统的树突状细胞算法的基于边界判断的清晰化划分方式对数据的排序敏感,并且存在着一定比例上的误判。提出的模型是基于模糊集合论框架下结合树突状细胞算法建立起来的一种全新的算法模型,实验证明基于模糊树突状细胞算法的实验结果一定程度上减低了误报率,不受排序的影响,更加有效和精确。     

Design optimization of fuzzy controllers in building structures using the crystal structure algorithm (CryStAl)

The current study uses a recently-developed metaheuristic method called Crystal Structure Algorithm (CryStAl) to achieve optimized vibration control in structural engineering. More specifically, this algorithm, which is inspired by the well-established crystallographic principles underlying the formation of crystalline solids in nature, is applied to the optimization of fuzzy logic controllers in building structures. To demonstrate the capability of this method in solving real engineering problems, two real-size building structures, one with three and the other with twenty stories, are considered. The fuzzy controllers are implemented through an active control system to control the seismically-induced vibrations of the structures intelligently. The evaluation criteria utilized to assess the overall performance of the optimization method applied to the fuzzy control system are presented and discussed. Through nonlinear structural analyses, the ductility, energy dissipation, and other nonlinear characteristics of the structures are also considered as the structural responses to be controlled. The computational results obtained from this novel metaheuristic algorithm are compared with those of the other expert systems from the optimization literature. The findings of this paper demonstrate that the Crystal Structure Algorithm is capable of outranking the other methods in the majority of considered cases.     

FURIA: an algorithm for unordered fuzzy rule induction

This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.     

基于模糊理论的软件项目调度算法

针对软件项目调度问题中信息的不确定性及资源分配的特殊性需要,提出了一种基于模糊理论的软件项目调度算法FSSA。该算法采用模糊数表示任务的工期并应用遗传算法产生任务的调度序列。实验结果表明,FSSA算法能在很短的时间内得到理想的结果,有一定的应用价值。     

融合粒子群和混合蛙跳的模糊C-均值算法

针对模糊聚类算法中存在的对初始值敏感、易陷入局部最优等问题,提出了一种融合粒子群算法和混合蛙跳算法的模糊C-均值聚算法。通过设计了一种新颖的搜索粒度系数,充分利用粒子群算法收敛速度快、局部搜索能力强的优点与混合蛙跳算法全局寻优能力强、跳出局部最优能力好的特点,同时对SFLA中更新算法进行了改进。实验结果表明,该算法提高了模糊聚类算法的搜索能力和聚类效果,在全局寻优能力、跳出局部最优能力、收敛速度等方面具有优势。     

A fast VQ codebook generation algorithm using codeword displacement

In this paper, we present a fast codebook generation algorithm called CGAUCD (Codebook Generation Algorithm Using Codeword Displacement) by making use of the codeword displacement between successive partition processes. By implementing a fast search algorithm named MFAUPI (Modified Fast Algorithm Using Projection and Inequality) for VQ encoding in the partition step of CGAUCD, the codebook generation time can be further reduced significantly. Using MFAUPI, the computing time of CGAUCD can be reduced by a factor of 4.7–7.6. Compared to Generalized Lloyd Algorithm (GLA), our proposed method can reduce the codebook generation time by a factor of 35.9–121.2. Compared to the best codebook generation algorithm to our knowledge, our approach can further reduce the corresponding computing time by 26.0–32.8%. It is noted that our proposed algorithm can generate the same codebook as that produced by the GLA. The superiority of our method is more remarkable when a larger codebook is generated.     

一种遗传模糊聚类算法及其应用

研究一种基于遗传算法的模糊聚类方法,即将遗传算法得到的聚类中心作为模糊C-均值（FCM）聚类算法初值,这样既可以克服FCM算法对初始中心敏感的缺点,也可以解决遗传算法只能找到近似解的问题。将算法用于通信信号的星座聚类,根据聚类有效性函数自适应地确定聚类中心,并完成信号类型的识别。仿真实验证明,当存在较小的定时误差时,算法对PSK和QAM信号仍然是有效的。     

基于遗传模糊聚类的医学超声图像分割方法

医学超声成像技术以其实时性、无损性与廉价性等优点被广泛应用于医疗诊断,但由于其固有的斑点噪声和与组织相关的纹理特性使得医学超声图像的分割一直是一个难题。模糊C均值聚类算法（FCM）具有较强的抗噪声能力,能够较好地完成医学超声图像的分割任务,但其局限性在于对聚类中心的初值较敏感,当随机选取初始聚类中心时,很有可能使分割过程陷入局部极小,影响分割结果。利用遗传算法（GA）能够寻找全局最优解的特点,提出一种基于遗传算法寻找初始聚类中心的模糊聚类方法,应用于医学超声图像分割并取得了良好效果。     

最大模糊频繁模式挖掘算法

针对有效模式挖掘的组合爆炸及挖掘结果信息如何有效表达的问题,提出了一种基于“核心-牵引”结构的修剪候选模式和考虑项目不确定性的最大模糊模式挖掘算法（MFFP-Tree）。首先,综合分析项目的模糊性,提出模糊支持度,分析项目在事务数据集中的模糊权重,依据模糊修剪策略修剪候选项集;其次,仅扫描数据集一次,就能成功构建模糊模式挖掘树,依据模糊剪枝策略减少模式提取的开销,采用FFP-array阵列结构使得搜索方式更精简,从而进一步降低时空开销。根据基准数据集的实验结果,与最大模式挖掘算法PADS和FPMax^*对比分析,MFFP-Tree挖掘出的最大模糊模式能够更准确地反映项目与项目之间的关系;算法的时间复杂度能减半甚至低1个数量级;算法的空间复杂度降低1~2个数量级。     

基于区间相似度的模糊时间序列预测算法

针对现有模糊时间序列预测算法无法适应预测中新关系出现的问题,提出了一种基于区间相似度的模糊时间序列预测（ISFTS）算法。首先,在模糊理论的基础上,采用基于均值的方法二次划分论域的区间,在论域区间上定义相应模糊集将历史数据模糊化;然后建立三阶模糊逻辑关系并引入逻辑关系相似度的计算公式,计算未来数据变化趋势值得到预测的模糊值;最后对预测模糊值去模糊化得到预测的确定值。由于ISFTS算法是预测数据变化趋势,克服了目前预测算法的逻辑关系的缺陷。仿真实验结果表明,与同类的预测算法相比,ISFTS算法预测误差更小,在误差相对比(MAPE)、绝对误差均值(MAE)和均方根误差(RMSE)三项指标上均优于同类的对比算法,因此ISFTS算法在时间序列预测中尤其是大数据量情况下的预测具有更强的适应性。     

贪心核加速动态规划算法求解折扣{0-1}背包问题

针对现有动态规划算法求解折扣{0-1}背包问题（D{0-1}KP）缓慢的问题，基于动态规划思想并结合新型贪心修复优化算法（NGROA）与核算法，通过缩小问题规模加速问题求解来提出一种贪心核加速动态规划（GCADP）算法。首先利用NGROA对问题进行贪心求解，得到非完整项；然后通过计算得到模糊核区间的半径和模糊核区间范围；最后对于模糊核区间内的物品及同一项集内的物品利用基础动态规划（BDP）算法求解。实验结果表明：GCADP算法适用于求解D{0-1}KP，且在求解速度上相比BDP算法平均提升了76.24%，相比FirEGA算法平均提升了75.07%。     

Size optimization of space trusses using Big Bang–Big Crunch algorithm

A Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm is employed for optimal design of truss structures. HBB–BC is compared to Big Bang–Big Crunch (BB–BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB–BC method compared to other heuristic algorithms.     

镇村公交下一种新型智能公交到站时间预测算法

提出了一种新型模糊隶属度算法,针对镇村公交线路进行公交到站时间预测。算法在速度集Vmin,Vmid1, Vmid2,...Vmidn-2,Vmidmax层面上对GPS公交运行数据进行分析,用模糊策略标定不同速度所属速度集,并用r解释不同速度集下公交行驶道路百分比,最终采用GPS历史数据和即时数据预测公交到站时间。结果表明,新的公交运行数据可进行算法更新以保证公交到站预测精度大于95%。     

改进的混合蛙跳算法

为提高混合蛙跳算法在优化问题求解中的性能,提出了一种改进混合蛙跳算法。改进算法在原算法基础上加入了变异算子,并根据算法进化过程的不同阶段和进化过程中候选解分布情况,利用模糊控制器对变异算子的变异尺度进行调整,实现了变异算子在解空间中搜索范围的动态调整。通过对优化问题中4个典型测试函数的仿真实验表明,与基本蛙跳算法和已有改进算法相比,改进算法在寻优精度、收敛速度和求解成功率上均有一倍以上的提高,尤其在高维复杂优化问题求解中体现出较强的寻优能力。     

A hybrid genetic algorithm for the job shop scheduling problems

The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. We design a scheduling method based on Single Genetic Algorithm (SGA) and Parallel Genetic Algorithm (PGA). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, the initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling methods based on genetic algorithm are tested on five standard benchmark JSSP. The results are compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement in solution quality. The superior results indicate the successful incorporation of a method to generate initial population into the genetic operators.     

Induction of descriptive fuzzy classifiers with the Logitboost algorithm

Recently, Adaboost has been compared to greedy backfitting of extended additive models in logistic regression problems, or “Logitboost". The Adaboost algorithm has been applied to learn fuzzy rules in classification problems, and other backfitting algorithms to learn fuzzy rules in modeling problems but, up to our knowledge, there are not previous works that extend the Logitboost algorithm to learn fuzzy rules in classification problems.In this work, Logitboost is applied to learn fuzzy rules in classification problems, and its results are compared with that of Adaboost and other fuzzy rule learning algorithms. Contradicting the expected results, it is shown that the basic extension of the backfitting algorithm to learn classification rules may produce worse results than Adaboost does. We suggest that this is caused by the stricter requirements that Logitboost demands to the weak learners, which are not fulfilled by fuzzy rules. Finally, it is proposed a prefitting based modification of the Logitboost algorithm that avoids this problem     

求解多背包问题的混合遗传算法

针对多背包问题最优解的求解,设计了一种新的价值密度;在此基础上结合传统的贪心算法,提出了一种求解多背包问题的混合遗传算法。该算法采用整数编码,并采用轮盘赌选择方法,对背包资源利用不足的可行解进行修正处理,对不可行解进行修复处理。并在大量的数值实验的基础上,将该方法与传统方法及简单遗传算法进行比较,实验结果表明,该混合遗传算法提高了问题求解的速度和精度,有一定的优越性。     

一种协同的可能性模糊聚类算法

模糊C-均值聚类（FCM）对噪声数据敏感和可能性C-均值聚类（PCM）对初始中心非常敏感易导致一致性聚类。协同聚类算法利用不同特征子集之间的协同关系并与其他算法相结合,可提高原有的聚类性能。对此,在可能性C-均值聚类算法（PCM）基础上将其与协同聚类算法相结合,提出一种协同的可能性C-均值模糊聚类算法（C-FCM）。该算法在改进的PCM的基础上,提高了对数据集的聚类效果。在对数据集Wine和Iris进行测试的结果表明,该方法优于PCM算法,说明该算法的有效性。     

Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm

Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them.