基于迁徙策略的PSO集成及其在序列模体识别中的应用

对优化问题和游牧民族迁徙行为的比较研究表明,两者存在相似之处,文中模拟了游牧民族迁徙机制,提出了一种迁徙策略,并且与粒子群优化算法相结合,提高了算法摆脱局部极值的能力,通过集成技术,充分发挥各种粒子群优化算法的优点,实现协同演化,提高了算法的全局搜索能力。最后应用于求解计算分子生物学中的一个复杂问题——生物序列模体识别,实验表明,该算法是有效的。     

Modified binary particle swarm optimization

This paper presents a modified binary particle swarm optimization （BPSO） which adopts concepts of the genotype-phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. Experimental results show that the modified BPSO outperforms the original BPSO algorithm.     

An ant colony algorithm for solving Max-cut problem

Max-cut problem is an NP-complete and classical combinatorial optimization problem that has a wide range of applications in different domains, such as bioinformatics, network optimization, statistical physics, and very large scale integration design. In this paper we investigate the capabilities of the ant colony optimization （ACO） heuristic for solving the Max-cut problem and present an AntCut algorithm. A large number of simulation experiments show that the algorithm can solve the Max-cut problem more efficiently and effectively.     

Novel evolutionary algorithm for global numerical optimization with continuous variables

Evolutionary algorithms (EA) are a class of general optimization algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. In this paper, a novel evolutionary algorithm is proposed to solve global numerical optimization with continuous variables. In order to make the algorithm more robust, the initial population is generated by combining determinate factors with random ones. And a decent scale function is designed to tailor the crossover operator so that it can not only find the decent direction quickly but also keep scanning evenly in the whole feasible space. In addition, to improve the performance of the algorithm, a mutation operator which increases the convergence-rate and ensures the convergence of the proposed algorithm is designed. Then, the global convergence of the presented algorithm is proved at length. Finally, the presented algorithm is executed to solve 24 benchmark problems. And the results show that the convergence-rate is noticeably increased by our algorithm.     

基于模糊法和神经网络的增压开采方案的优选研究

多属性决策问题广泛存在于经济、管理、社会等各个领域中,如方案优选、工厂选址、资源分配、投资决策、项目评估、质量评估、科研成果评价、人才考核、产业部门发展排序、经济效益综合评价等,因此多属性决策理论及方法有着广阔的应用前景．对多属性决策理论、方法及其应用进行系统的研究具有重要的学术价值和实践意义．基于模糊理论的模糊优选方法是一种用于涉及模糊因素决策问题的优选方法,采用模糊优选方法可以较好地解决方案优选中的模糊性问题．本文以ANN、模糊理论为基础,对引入模糊优选方法解决气藏增压开采方案优选问题进行研究．     

采用优化的Bass模型对中国IPv4标准扩散趋势研究

在介绍描述创新扩散的Bass模型基础上,提出了优化模型的方法--采用蚁群算法作为Bass模型的参数估计算法,并以中国互联网IPv4标准的扩散为例.采用优化的Bass模型对其扩散趋势进行了深入研究.通过与传统算法的比较,证明采用蚁群算法可以提高Bass模型参数估计的精度,并通过对模型实证研究结果的分析得出标准扩散作为一种特殊的技术创新扩散.遵循其一般扩散规律,采用优化的Bass模型对标准扩散问题进行研究效果会更好.最后对中国互联网IPv4标准扩散趋势进行了预测并提出了策略建议.     

包容核废物的钛酸锶人造岩石SHS-致密化的影响因素

采用自蔓延高温合成(SHS)与加压致密一体化技术制备了包容锶核素(Sr2+)的钛酸锶(SrTiO3)人造岩石固化体. 通过实验分析SHS产物致密化的影响因素,确定了钛酸锶反应体系自蔓延高温合成时的最佳工艺参数:预制块相对密度为54%左右;预压力为10MPa,加压时间为4s;致密化时高压压力为300MPa,高压保压时间为8s.     

纳米钨粉的烧结工艺

采用催化-凝胶法制备的平均粒径60nm的纳米钨粉为原料,经钢模压制成生坯,用高温膨胀仪测定了纳米钨粉坯体的烧结收缩动力学曲线;然后分别测定了不同烧结温度和烧结时间下烧结体晶粒尺寸和相对密度的变化.结果表明,纳米钨粉的坯体在200℃开始收缩,1300℃基本停止收缩.从1000℃到1200℃,其相对密度提高了24%,是致密化过程最快的阶段.在1200℃×120min的烧结工艺下得到烧结体相对密度为95%,晶粒尺寸为5μm的钨材.     

量子可逆逻辑电路最小代价综合算法

采用位运算构造了一个完备的Hash函数,提出了基于Hash表的新型量子可逆逻辑电路综合算法.利用该算法可对多种量子门采用任意量子代价标准,以较高的效率生成最优的量子可逆逻辑电路.按照国际同行认可的三变量可逆函数测试标准,该算法不仅能够生成全部最优电路,而且运行速度远远超过其他算法.实验结果表明,该算法在同等计算环境下,以目前最好结果为基准,按最小量子代价标准,综合电路的平均速度是最好结果的472.5倍.     

施振功率与温度对工业纯铝凝固组织的影响

在工业纯铝的凝固过程中引入超声场,探讨了超声细化晶粒的机理,并具体研究了超声功率、导入声波的熔体温度区间对铸锭凝固组织的影响规律. 实验结果表明:铝熔体经超声处理后,凝固组织均得到显著细化,这主要取决于超声的空化效应和声流效应;超声功率增大时,组织细化程度提高,功率达到170W时细化效果最好,继续增大功率则细化作用减弱;施振温度区间对凝固组织的影响也有相似的规律,在合理的温度导入声波,晶粒能细化到最佳程度.