An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others.

Comparison of Websites Employing Search Engine Optimization and Live Data

This study compares websites that take live data into account using search engine optimization(SEO).A series of steps called search engine optimization can help a website rank highly in search engine results.Static websites and dynamic websites are two different types of websites.Static websites must have the necessary expertise in programming compatible with SEO.Whereas in dynamic websites,one can utilize readily available plugins/modules.The fundamental issue of all website holders is the lower level of page rank,congestion,utilization,and exposure of the website on the search engine.Here,the authors have studied the live data of four websites as the real-time data would indicate how the SEO strategy may be applied to website page rank,page difficulty removal,and brand query,etc.It is also necessary to choose relevant keywords on any website.The right keyword might assist to increase the brand query while also lowering the page difficulty both on and off the page.In order to calculate Off-page SEO,On-page SEO,and SEO Difficulty,the authors examined live data in this study and chose four well-known Indian university and institute websites for this study:www.caluniv.ac.in,www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in.Using live data and SEO,the authors estimated the Off-page SEO,On-page SEO,and SEO Difficulty.It has been shown that the Off-page SEO of www.caluniv.ac.in is lower than that of www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in by 9%,7%,and 7%,respectively.On-page SEO is,in comparison,4%,1%,and 1%more.Every university has continued to keep up its own brand query.Additionally,www.caluniv.ac.in has slightly less SEO Difficulty compared to other websites.The final computed results have been displayed and compared.

Harmony search optimization applied to reservoir engineering assisted history matching

Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages:(1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient.(2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching.(3) The integration between the three components(harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions.(4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms(genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.

PID Parameters for Tuning and Optimization of a Turbine EngineBased on the Simplex Search Method

A PID parameters tuning and optimization method for a turbine engine based on the simplex search method was proposed. Taking time delay of combustion and actuator into account, a simulation model of a PID control system for a turbine engine was developed. A performance index based on the integral of absolute error （IAE） was given as an objective function of optimization. In order to avoid the sensitivity that resulted from the initial values of the simplex search method, the traditional Ziegler-Nichols method was used to tune PID parameters to obtain the initial values at first, then the simplex search method was applied to optimize PID parameters for the turbine engine. Simulation results indicate that the simplex search method is a reasonable and effective method for PID controller parameters tuning and optimization.

Website Search Engine Optimization: Geographical and Cultural Point of View

The concept of Webpage visibility is usually linked to search engine optimization (SEO), and it is based on global in-link metric [1]. SEO is the process of designing Webpages to optimize its potential to rank high on search engines, preferably on the first page of the results page. The purpose of this research study is to analyze the influence of local geographical area, in terms of cultural values, and the effect of local society keywords in increasing Website visibility. Websites were analyzed by accessing the source code of their homepages through Google Chrome browser. Statistical analysis methods were selected to assess and analyze the results of the SEO and search engine visibility (SEV). The results obtained suggest that the development of Web indicators to be included should consider a local idea of visibility, and consider a certain geographical context. The geographical region that the researchers are considering in this research is the Hashemite kingdom of Jordan (HKJ). The results obtained also suggest that the use of social culture keywords leads to increase the Website visibility in search engines as well as localizes the search area such as google.jo, which localizes the search for HKJ.

Search Engine Optimization,Competitive Advantage and Market Performance of Registered Tours and Travel Agencies in Nairobi City County,Kenya

COVID-19 is a devastating pandemic with widespread negative health,social,and economic consequences.Due to drastic changes in the business environment of tour and travel agencies,firms and marketing managers can now use search engine optimization to effectively position themselves.The study’s main goal is to evaluate the effect of search engine optimization on the market performance of registered tours and travel agencies in Nairobi County,Kenya.Kenya’s tourism ministry and state government work hard to improve the business climate for tour and travel companies.Despite the overall positive image,international tourist market growth rates in Kenya have been 3.5 percent slower from 2017 to 2019 compared to previous years.This was further aggregated by the onset of the COVID-19 pandemic in the year 2020,when the growth rate of tours and travel agencies fell by 65%.The study’s main goal is to evaluate the effect of search engine optimization on the market performance of registered tours and travel agencies in Nairobi County,Kenya.This study adopted a positivist philosophy.Both descriptive and explanatory research designs were used.A self-administered semi-structured questionnaire was used to collect data from 324 registered tours and travels agencies picked from and a sample of 179 were used.Data analysis included the development and interpretation of both descriptive and inferential statistics,such as frequencies,mean,percentages,and standard deviation,and was presented using tables and numerical values.The results of regression analysis established that search engine optimization had a positive and significant effect on market performance of the registered tours and travel agencies picked from a sample of 179.The study recommends that agency management ensure that the firm’s website is easily accessible in order to improve agency performance.Using the internet to gain a large market share can assist tours and travel agencies in improving the performance and income of their websites.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

RESEARCH ON OPTIMIZING THE MERGING RESULTS OF MULTIPLE INDEPENDENT RETRIEVAL SYSTEMS BY A DISCRETE PARTICLE SWARM OPTIMIZATION

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

搜索引擎Google的检索功能及PageRank技术分析

本文介绍了搜索引擎 Google的特点和检索功能 ,并重点分析了其 Page

Google搜索引擎的PageRank技术及其优化研究

本文重点分析了Google搜索引擎的PageRank技术的实现算法、优点及不足之处,并提出利用行为反馈再排序机制进行优化。

面向工程数据检索的ElasticSearch索引优化策略

随着生产制造业的发展,各行业在生产制造的过程中都会产生大量的工程数据,现代工程领域的数据检索需求要求能够通过关键字快速且准确检索出相应的结果,利用ElasticSearch可以实现工程数据的检索,但是其性能方面还有优化的空间。为了解决这个问题,本文对ElasticSearch的底层原理进行深入研究,在ElasticSearch的索引创建、索引分片以及索引段合并方面进行优化。首先对ElasticSearch的分词器进行修改并配置自定义词典,其次提出基于集群节点性能与索引数据量大小的索引分片策略,最后,根据节点性能对索引段合并的时机进行优化。通过基于地铁工程数据的检索进行实验,实验结果表明,改进的方法确实能够提高ElasticSearch的数据写入与查询性能。

Google搜索引擎的核心——PageRank算法综述

搜索引擎技术的发展是随着电子技术不断进步而形成的信息数字化和数据网络化的必然产物。Google搜索引擎依靠其PageRank机制及收敛算法一直处于该领域的领先地位。本文介绍了PageR- ank算法的起源和发展。对其概念与方法进行了阐述,并讨论了它的应用情况。

An Effective Fault Diagnosis Method for Aero Engines Based on GSA-SAE

The health status of aero engines is very important to the flight safety.However,it is difficult for aero engines to make an effective fault diagnosis due to its complex structure and poor working environment.Therefore,an effective fault diagnosis method for aero engines based on the gravitational search algorithm and the stack autoencoder(GSA-SAE)is proposed,and the fault diagnosis technology of a turbofan engine is studied.Firstly,the data of 17 parameters,including total inlet air temperature,high-pressure rotor speed,low-pressure rotor speed,turbine pressure ratio,total inlet air temperature of high-pressure compressor and outlet air pressure of high-pressure compressor and so on,are preprocessed,and the fault diagnosis model architecture of SAE is constructed.In order to solve the problem that the best diagnosis effect cannot be obtained due to manually setting the number of neurons in each hidden layer of SAE network,a GSA optimization algorithm for the SAE network is proposed to find and obtain the optimal number of neurons in each hidden layer of SAE network.Furthermore,an optimal fault diagnosis model based on GSA-SAE is established for aero engines.Finally,the effectiveness of the optimal GSA-SAE fault diagnosis model is demonstrated using the practical data of aero engines.The results illustrate that the proposed fault diagnosis method effectively solves the problem of the poor fault diagnosis result because of manually setting the number of neurons in each hidden layer of SAE network,and has good fault diagnosis efficiency.The fault diagnosis accuracy of the GSA-SAE model reaches 98.222%,which is significantly higher than that of SAE,the general regression neural network(GRNN)and the back propagation(BP)network fault diagnosis models.

图书馆网络信息检索方法与技巧

随着互联网技术的飞速发展,图书馆信息服务模式正经历着从传统的纸质资料查阅向数字化、网络化方向转变。网络信息检索已成为图书馆服务的重要组成部分,为用户提供高效、便捷的知识获取途径。该文旨在探讨图书馆网络信息检索的方法与技巧,包括搜索引擎的优化使用、数据库资源的高效挖掘、信息筛选与评估策略,以及个性化信息服务的实现,旨在提升用户的信息检索便利度,促进学术研究与服务学习型社会建设。

对网页PageRank算法的改进

分析了著名搜索引擎 Google采用的 Page Rank算法 ,指出其偏重旧网页、忽视专业站点以及对网页中的超链接评估不恰当等不足之处 .改进算法考虑了网页日期这一重要因素 ,并重新计算网页中超链接对网页的影响 .网页结构中蕴涵着丰富的信息 ,在 href、title等标记中文字对网页主题有重要作用 ,利用结构标记可以辅助判断网页的主题内容 .试验结果表明 。

PageRank算法研究

深入剖析了著名搜索引擎Google的关键技术PageRank算法,介绍分析了该算法的当前发展现状。并针对PageRank算法容易出现主题漂移现象,利用提出的二阶相似度改进算法。实验表明,改进的算法有利于减少主题漂移现象,提高用户对检索结果的满意度。

PageRank算法的分析及其改进

在分析PageRank算法存在偏重旧网页、主题漂移、网页权值均分、忽视用户浏览兴趣现象的基础上,对其进行改进,考虑网页修改日期、网页文本信息、网站权威度、用户兴趣度等重要因素,重新计算网页PR值。实验结果表明,改进算法可提高搜索引擎对网页排序的准确度,以及用户对检索结果的满意度。

PageRank-Pro——一种改进的网页排序算法

根据网页点击信息改进了原有的PageRank算法,利用Seidel迭代算法加快了迭代收敛过程.实验结果表明,改进后的迭代算法效率提高23%左右.