外星人的探索

对于“探索”这个命题．我首先想到的是未知事物,未解之谜。比如胡夫金字塔的震撼,百慕大三角的神秘．复活岛巨石阵的由来．种种猜测牵引着人们对不解之谜的探索。     

PC-1500袖珍计算机死机复活程序

<正> 在调试汇编语言程序时,经常出现死机状态,通常用“切断电源”使主机正常;但操作“PC-1500机”死机复活“程序”之后,一旦主机发生锁死,按BREAK键即可复活,而不用切断电源。一、PC-1500机“死机复活”程序用汇编语言编写的“死机复活”程序如下:     

PC-1500“死机复活”程序

在调试汇编语言程序时常出现死机状态,通常是用“切断电源”使主机恢复正常。操作PC-1500“死机复活”程序之后,一旦主机发生锁死,按“BREAK”键可复活,不用切断电源。1 PC-1500“死机复活”(汇编语言)     

福建：移动携手平潭综合实验区35亿元助建海西信息强岛

近日,福建移动与平潭综合实验区管委会签署《关于共同落实和推进海峡西岸经济区建设的战略合作协议》。未来5年,福建移动将在平潭综合实验区投入35亿元,努力提升平潭综合实验区社会信息化发展水平和信息产业规模,将平潭综合实验区建设成为“开放活力岛”、“国际旅游岛”、“科技智慧岛”和“生态宜居岛”。     

一种基于量子染色体变异的移动机器人路径规划融合算法

为了解决机器人路径规划中的“局部最小”问题,提出了一种基于量子染色体变异的人工势场法和栅格法相融合的移动机器人路径规划算法.首先,对人工势场的斥力场进行改进,然后利用融合的人工势场法和栅格法对路径进行规划,产生初始化种群,最后利用量子比特对染色体编码、利用量子染色体变异对种群个体进行更新,完成最佳路径搜索.仿真实验表明,本文提出的融合算法能够有效地避开障碍物,稳定地产生移动机器人的最佳规划路径,提高了种群质量和收敛速度,适合于求解复杂优化问题,达到了预期效果.     

感受和美的未来 上海世博会日本馆游记

“紫蚕岛”，一个像生命体那样会呼吸、对环境友好的建筑 从5号门入园便进入集中了亚洲各国场馆的A区，步行没多远就能看到那只醒目而独特的紫色蚕子。“紫蚕岛”不仅仅独特的外形让人感受到它是一个会呼吸、拥有生命的个体．而且由于采用了环境控制技术，使得光、水、空气等自然资源被最大限度利用，也把“紫蚕岛”变成一座名副其实的“像生命体那样会呼吸、对环境友好的建筑”。     

西门子S7-200PLC应用于大容量镍镉蓄电池组智能维护系统

利用西门子公司小型PLC为控制中心,5.7英寸触摸屏为操作显示平台,加上晶闸管的整流和有源逆变技术组成了大容量镉镍电池组的容量复活设备即电池活化装置。它的出现不但克服了镉镍电池长期使用造成的“记忆”效应,而且提高了蓄电池的使用寿命。     

一种新的图像恢复遗传算法

针对简单遗传算法在进行图像恢复时,存在“过早收敛”现象,及计算量过大的问题,设计了一种新的二维染色体编码方法,并将传统遗传算法与模拟退火算法相结合。实验结果表明,该方法能较好克服“过早收敛”,降低计算复杂度,改善退化图像恢复质量。     

凤凰涅磬 专访OCZ副总裁Alex Mei

说到OCZ，DIY玩家几乎都知道这个品牌。它致力于发烧级存储产品的研发与生产，一直扮演着“SSD固态硬盘先驱者”的角色，去年OCZ终于因为资金问题宣告破产气今年3月份，OCZ复活了!东芝集团宣布并购了OCZ，而在刚过去的Computex 2014台北电脑展上，OCZ向消费者展示了旗下多款SSD固态硬盘产品。是什么原因让OCZ原地满血复活？带着种种疑问，本刊记者采访了OCZ副总裁Alex Mei。     

改进的模拟退火和遗传算法求解TSP问题

对遗传算法和模拟退火算法的特点进行了比较,阐述了遗传算法与模拟退火算法集合的必要性。提出了一个用于求解TSP问题的改进的模拟退火和遗传算法。利用遗传算法的全局搜索能力弥补了模拟退火算法容易陷入局部最优的问题。在遗传算法中改进了传统的交叉机制,利用父代染色体与子代染色体进行交叉,解决了传统遗传算法中存在的“早熟”问题。针对模拟退火算法收敛速度慢等问题,提出了新的解生成机制和改良算法,提高了算法的收敛速度。实验测试的结果表明,该方法具有较好的收敛效果和更高的稳定性。     

基于遗传算法的IP网流量优化算法

流量工程通过对IP网流量的优化以更有效利用网络资源。现有研究的一个重要方向是把流量工程问题用线性规划建模,并利用传统的Simplex算法求得最优解,文章提出了一种基于遗传算法的求解方法,从一组随机选取的解(染色体)出发,经过交叉、突变等基因进化操作和多代的选择,最终达到预先设定的适应度准则;给出仿真结果和相关讨论;显示该文算法在运算量,处理动态流量需求等方面有较好的应用前景。     

Genetic algorithms based approach to database vertical partition

Vertical partition clusters attributes of a relation to generate fragments suitable for subsequent allocation over a distributed platform with the goal of improving performance. Vertical partition is an optimization problem that can resort to genetic algorithms (GA). However, the performance of the classical GA application to vertical partition as well as to similar problems such as clustering and grouping suffers from two major drawbacks—redundant encoding and non-group oriented genetic operations. This paper applies the restricted growth (RG) string Ruskey (1993) constraint to manipulate the chromosomes so that redundant chromosomes are excluded during the GA process. On RG string compliant chromosomes, the group oriented crossover and mutation become realizable. We thus propose a novel approach called Group oriented Restricted Growth String GA (GRGS-GA) which incorporates the two above features. Finally, we compare the proposed approach with a rudimental RG string based approach and a classical GA based approach. The conducted experiments demonstrate a significant improvement of GRGS-GA on partition speed and result, especially for large size vertical partition problems.     

基于遗传和禁忌搜索混合算法的预制生产调度的研究

遗传算法是一种全局搜索能力较强的元启发式算法,可通过不断进化种群得到最优或近优解;但是遗传算法的局部搜索能力较差,容易发生早熟收敛问题。因此为了克服遗传算法早熟收敛的问题,考虑到禁忌搜索算法的局部搜索能力较强的优势,提出了一种遗传和禁忌搜索的混合算法解决预制生产流水车间的提前和拖期惩罚问题。该混合算法是在遗传算法每次迭代后,通过禁忌搜索改进当前种群中的最好染色体,并替换种群中适应度值最差的染色体。经实验测试表明,所提出的混合算法的性能更优,更容易得到全局最优解或近优解。     

A new hybrid GA solution to combinatorial optimization problems— an application to the multiprocessor scheduling problem

The multiprocessor scheduling problem is one of the classic examples of NP-hard combinatorial optimization problems. Several polynomial time optimization algorithms have been proposed for approximating the multiprocessor scheduling problem. In this paper, we suggest a geneticizedknowledge genetic algorithm (gkGA) as an efficient heuristic approach for solving the multiprocessor scheduling and other combinatorial optimization problems. The basic idea behind the gkGA approach is that knowledge of the heuristics to be used in the GA is also geneticized alongiside the genetic chromosomes. We start by providing four conversion schemes based on heuristics for converting chromosomes into priority lists. Through experimental evaluation, we observe that the performance of our GA based on each of these schemes is instance-dependent. However, if we simultaneously incorporate these schemes into our GA through the gkGA approach, simulation results show that the approach is not problem-dependent, and that the approach outperforms that of the previous GA. We also show the effectiveness of the gkGA approach compared with other conventional schemes through experimental evaluation. This work was presented, in part, at the Second International Symposium on Artifiical Life and Robotics, Oita, Japan, February 18–20, 1997     

A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems

Genetic Algorithm (GA) has found wide application in path optimization problem. In many fields such as navigating system, oil transportation, paths between the starting node and the termination node often have distinct number of relay-nodes, which leads to the corresponding chromosomes would have different length. We refer to chromosomes with non-consistent lengths as the variable-length chromosomes. This paper first investigated GAs with variable-length chromosomes widely used and found that Same Point (SP) crossover is the most popular crossover mechanism. Then, a new crossover mechanism called Same Adjacency (SA) is proposed for GA with variable-length chromosomes for path optimization problem, which outperforms GA with SP by a better search capability as the mathematical analysis shows. The simulation study indicates that GAs with our crossover operators could obtain a better solution, as compared to GAs with SP, while still being able to converge fast in different networks with varied sizes.     

Dual hierarchical genetic-optimal control: A new global optimal path planning method for robots

A new two-stage analytical-evolutionary algorithm considering dynamic equations is presented to find global optimal path. The analytical method is based on the indirect open loop optimal control problem and the evolutionary method is based on genetic algorithm (GA). Initial solutions, as start points of optimal control problem, are generated by GA to be used by optimal control. Then, a new sub-optimal path is generated through optimal control. The cost function is calculated for every optimal solution and the best solutions are chosen for the next step. The obtained path is used by GA to produce new generation of start points. This process continues until the minimum cost value is achieved. In addition, a new GA operator is introduced to be compatible with optimal control. It is used to select the pair chromosomes for crossover. The proposed method eliminates the problem of optimal control (being trapped in locally optimal point) and problem of GA (lack of compatibility with analytical dynamic equations). Hence problem is formulated and verification is done by comparing the results with a recent work in this area. Furthermore effectiveness of the method is approved by a simulation study for spatial non-holonomic mobile manipulators through conventional optimal control and the new proposed algorithm.     

用GA优化模糊控制器及其应用

用遗传算法优化模糊控制器的隶属度函数,得到优化的模糊控制器。在遗传算法设计方面,采用十进制基因编码,减少了译码的麻烦,避免了常规情况下以二进制形式编码的染色体物理意义不明显和在交叉、变异操作中容易破坏本身特性的缺点。给出了一种比较实用的交叉、变异和选择的方法,不仅使得种群进化的操作计算变得简单,而且既保留了染色体中好的特性又优化了不良的染色体。全部程序由Matlab编程实现。针对某货船,将优化后的模糊控制器在Matlab的Simulink中进行了多种情况下的仿真研究。此外,对于种群规模、进化代数对模糊控制器性能的影响也做了对比的仿真研究。由仿真结果可知,遗传算法能够有效地提高模糊控制器的性能。     

Addressing a nonlinear fixed-charge transportation problem using a spanning tree-based genetic algorithm

In this paper, we consider the fixed-charge transportation problem (FCTP) in which a fixed cost, sometimes called a setup cost, is incurred if another related variable assumes a nonzero value. To tackle such an NP-hard problem, there are several genetic algorithms based on spanning tree and Prüfer number representation. Contrary to the findings in previous works, considering the genetic algorithm (GA) based on spanning tree, we present a pioneer method to design a chromosome that does not need a repairing procedure for feasibility, i.e. all the produced chromosomes are feasible. Also, we correct the procedure provided in previous works, which designs transportation tree with feasible chromosomes. We show the previous procedure does not produce any transportation tree in some situations. Besides, some new crossover and mutation operators are developed and used in this work. Due to the significant role of crossover and mutation operators on the algorithm’s quality, the operators and parameters need to be accurately calibrated to ensure the best performance. For this purpose, various problem sizes are generated at random and then a robust calibration is applied to the parameters using the Taguchi method. In addition, two problems with different sizes are solved to evaluate the performance of the presented algorithm and to compare that performance with LINGO and also with the solution presented in previous work.     

Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)

The optimal placement of electronic components on a printed circuit board (PCB) requires satisfying multiple conflicting design objectives as most of the components have different power dissipation, operating temperature, types of material and dimension. In addition, most electronic companies are currently emphasizing on designing a smaller package electronic system in order to increase the system performance. This paper presents a new self organizing genetic algorithm (SOGA) method for solving this multi-objective optimization problem. The SOGA can be viewed as a cascade of two GAs which consists of two steps fitness evaluation process to ensure that the fitness of selected chromosomes for each iteration process is optimally selected. The algorithm is developed based on weighted sum approach genetic algorithm (WSGA) where an inner loop GA is used to optimize the selection of weights of the WSGA. Experiments are conducted to evaluate the performance of SOGA. Four objective functions are formulated in the experiments which are temperature of components, area of PCB, high power component placement and high potential critical components distance. Comparisons of the performance of SOGA are made with two well known methods namely fixed weight GA (FWGA) and random weighted GA (RWGA). The results show that the SOGA gives a better optimal solution as compared to the other methods.     

求解VRP问题的一种基于交叉算子的改进型遗传算法

采用基于自然数编码染色体、改进型交叉算子并增加内外扰动策略,构造出一种改进型遗传算法。详细介绍了此算法的基本原理,并进行了代表性算例实验与结果分析。实验表明,该算法收敛速度快,有效地遏制了早熟收敛,防止了进化过程中最优解的退化,改善了遗传算法的性能,提高了算法优化效率,是求解车辆路径问题的一种有效算法。