Research Reports on Computer Science最新文献

筛选

英文中文

Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques 利用利维飞行和拥挤距离技术改进基于驾驶训练的优化算法

Research Reports on Computer Science Pub Date : 2024-05-08 DOI: 10.37256/rrcs.3120244384

Daniel Kwegyir, Michael Dugbartey Terkper, F. Effah, Emmanuel Kwaku Anto, Stacy Gyamfuah Lumor

{"title":"Improved Driving Training-Based Optimization Algorithm Using Levy Flight and Crowding Distance Techniques","authors":"Daniel Kwegyir, Michael Dugbartey Terkper, F. Effah, Emmanuel Kwaku Anto, Stacy Gyamfuah Lumor","doi":"10.37256/rrcs.3120244384","DOIUrl":"https://doi.org/10.37256/rrcs.3120244384","url":null,"abstract":"This study presents an improved version of the Driving Training-Based Optimization (DTBO) algorithm, the Improved Driving Training-Based Optimization (IDTBO). The work addresses fundamental issues in selecting drivers and learners for the conventional DTBO, which substantially impact the algorithm's accuracy and convergence speed. Two significant improvements are proposed: including the crowding distance technique for more diverse driver and learner selection and incorporating the Levy Flight distribution for better exploration and local optima avoidance. The IDTBO's performance is evaluated using twelve benchmark functions, including unimodal and high-dimensional multimodal optimization functions. The results indicate that IDTBO performs exceptionally well, with more extraordinary exploitation ability on unimodal functions and consistent achievement of the global optima. The proposed IDTBO demonstrated exceptional exploration capabilities on high-dimensional multimodal functions and performed competitively with other algorithms in the literature. From six functions, the IDTBO obtained zero optimal values. Again, the rate of convergence analysis demonstrates that IDTBO finds optimal solutions in fewer iterations, demonstrating its capacity to balance exploration and exploitation. To assess the strength of the IDTBO in solving real-world problems, the improved DTBO is further tested on two practical benchmark engineering problems. The IDTBO again produced a competitive performance against other algorithms in the literature. The study shows that IDTBO is a valuable metaheuristic algorithm that can tackle various real-world optimization problems.","PeriodicalId":495542,"journal":{"name":"Research Reports on Computer Science","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141001175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Based Fabric Defect Detection 基于深度学习的织物缺陷检测

Research Reports on Computer Science Pub Date : 2024-03-20 DOI: 10.37256/rrcs.3120244156

Syeda Rabia Arshad, Muhammad Khuram Shahzad

引用次数: 0