基于平均熵的自适应人工蜂群算法

针对基本人工蜂群算法容易陷入局部最优和早熟等问题,提出一种改进的人工蜂群算法(ASABC)。利用平均熵机制初始化种群,增加种群的多样性,避免算法陷入早熟;同时,采用自适应调节邻域搜索步长的策略来提高算法的局部搜索能力,提升算法的计算精度;为了平衡算法的全局搜索能力和局部搜索能力,引入自适应比例选择策略来代替人工蜂群算法的适应度比例选择方法。对8个标准测试函数的仿真实验结果表明,与3种常见的智能优化方法相比,改进的算法具有显著的局部搜索能力和较快的收敛速度。     

Incorporating user preferences in ant colony optimization for the next release problem

The selection of which requirements should be implemented in the next software release is an important and complex task in the software development process, considering the presence of budget constraints and other conflicting aspects. In this context, search based software engineering, has the main objective of applying automatic search methods to solve complex software engineering problems. However, most of these methods do not consider human expertise during the search, especially due to the difficulty in mathematically modeling the user's preferences. Consequently, the user can present some resistance or place little confidence in the final results, given that his/her knowledge and domain expertise was not properly considered in the solution construction. This paper aims at proposing an interactive model for the next release problem using ant colony optimization, where the user can define which requirements he/she would like to include or not in the next release. Employing humans and a simulator, an empirical study was performed that considers real-world and artificial instances. The achieved results demonstrate that the loss of score was, on average, 12% when it was compared with a solution with no human intervention. On the other hand, the algorithm generates solutions that have more than 80% of the met preferences, as defined by the users. Furthermore, the results showed that ACO can be an interesting choice as an interactive search engine, given the low quantity of interactions that are required to reach good solutions.     

P2P网络中的局域搜索免疫

针对P2P网络的特性,本文提出了一种局域搜索免疫。实验使用SIS病毒传播模型征BA无标度网络和实际网络中测试了局域搜索免疫策略,结果表明与其它免疫策略相比,该策略可以通过免疫相等或较少的节点来消灭病毒。     

A novel artificial bee colony algorithm with Powell's method

Artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions.     

搜索引擎在学术期刊编辑工作中的作用与应用策略

本文通过阐述分析网络信息资料库的特点,讲述了学术期刊编辑如何使用搜索引擎来更简单方便的检索工作所需要的信息,并点明了在使用搜索引擎时应注意的问题,从而使编辑在工作中减少无用功,提高工作效率。     

搜索引擎优化的作弊与防范

随着信息技术的飞速发展,人们越来越多的通过搜索引擎获取信息。快速增长的互联网信息在提供丰富的资源的同时也催生了大量的搜索引擎作弊的行为。本文先是通过对搜索引擎优化的作弊手段进行分析进而提出防范作弊的几种方法。     

The scaling problem in the pattern recognition approach to machine translation

Statistical machine translation (SMT) has proven to be an interesting pattern recognition framework for automatically building machine translations systems from available parallel corpora. In the last few years, research in SMT has been characterized by two significant advances. First, the popularization of the so called phrase-based statistical translation models, which allows to incorporate local contextual information to the translation models. Second, the availability of larger and larger parallel corpora, which are composed of millions of sentence pairs, and tens of millions of running words. Since phrase-based models basically consists in statistical dictionaries of phrase pairs, their estimation from very large corpora is a very costly task that yields a huge number of parameters which are to be stored in memory. The handling of millions of model parameters and a similar number of training samples have become a bottleneck in the field of SMT, as well as in other well-known pattern recognition tasks such as speech recognition or handwritten recognition, just to name a few. In this paper, we propose a general framework that deals with the scaling problem in SMT without introducing significant time overhead by means of the combination of different scaling techniques. This new framework is based on the use of counts instead of probabilities, and on the concept of cache memory.     

AJAX搜索引擎研究

互联网发展迅速,搜索引擎在网络应用中开始显现主导地位。下一代网络技术的代表是AJAX技术．由于AJAX技术开发的页面具有许多新属性,传统的搜索引擎不能够查找到AJAX技术开发的页面,该文分析了AJAX搜索引擎的概念,从搜索引擎的原理和AJAX技术分析出发,并与采用了AJAX技术的传统搜索引擎作对比,研究AJAX搜索引擎的特点。     

基于最短道路的城市公交智能咨询系统的研究与实现

研究基于最短道路的城市公交智能查询系统,首先针对城市公交线路网络图建立数学模型;然后研究基于上述数学模型的数据库建立方法和相应的算法设计,和以Dijkstra算法为基础的、基于道路最短的公交线路查询算法,并通过简单模拟数据验证算法的有效性;最后简要介绍按此思想设计实现的城市公交智能查询系统.