Abstract To address the realistic problem of seriously reducing distribution efficiency and increasing distribution cost caused by road traffic congestion, this paper constructs a time-dependent speed describing vehicle travel speed and road traffic flow by simulating the change of urban traffic flow, to establish a vehicle route problem model considering traffic flow with distribution cost and customer satisfaction as optimization objectives. To solve this problem, a hyper-heuristic algorithm based on Tabu search is designed in this paper, in which the underlying search operator is selected more efficiently by a high-level heuristic strategy. In addition, the correctness of the model and the effectiveness of the algorithm are verified by conducting simulation experiments on several benchmark sets. Experiment results are shown as the travel speed of the vehicle increases, the average customer satisfaction in lc1-type instances increases to 0.94. And the impact of urban traffic changes on logistics costs and customer satisfaction is further analyzed.
{"title":"Hyper-heuristic algorithm for traffic flow-based VRP with simultaneous delivery and pickup","authors":"Wang Zheng, Liu Jinlong, Zhang Jingling","doi":"10.1093/jcde/qwad097","DOIUrl":"https://doi.org/10.1093/jcde/qwad097","url":null,"abstract":"Abstract To address the realistic problem of seriously reducing distribution efficiency and increasing distribution cost caused by road traffic congestion, this paper constructs a time-dependent speed describing vehicle travel speed and road traffic flow by simulating the change of urban traffic flow, to establish a vehicle route problem model considering traffic flow with distribution cost and customer satisfaction as optimization objectives. To solve this problem, a hyper-heuristic algorithm based on Tabu search is designed in this paper, in which the underlying search operator is selected more efficiently by a high-level heuristic strategy. In addition, the correctness of the model and the effectiveness of the algorithm are verified by conducting simulation experiments on several benchmark sets. Experiment results are shown as the travel speed of the vehicle increases, the average customer satisfaction in lc1-type instances increases to 0.94. And the impact of urban traffic changes on logistics costs and customer satisfaction is further analyzed.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"13 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136232977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The Snow Ablation Optimizer (SAO) is a new metaheuristic algorithm proposed in April 2023. It simulates the phenomenon of snow sublimation and melting in nature and has a good optimization effect. The SAO proposes a new two-population mechanism. By introducing Brownian motion to simulate the random motion of gas molecules in space. However, as the temperature factor changes, most water molecules are converted into water vapor. Which breaks the balance between exploration and exploitation, and reduces the optimization ability of the algorithm in the later stage. Especially in the face of high-dimensional problems, it is easy to fall into local optimal. In order to improve the efficiency of the algorithm, this paper proposes an improved Snow Ablation Optimizer with Heat Transfer and Condensation Strategy(SAOHTC). Firstly, this article proposes a heat transfer strategy. Utilizes gas molecules to transfer heat from high to low temperatures and move their positions from low to high temperatures. Causing individuals with lower fitness in the population to move towards individuals with higher fitness, thereby improving the optimization efficiency of the original algorithm. Secondly, a condensation strategy is proposed. Which can transform water vapor into water by simulating condensation in nature, improve the deficiency of the original two-population mechanism. improve the convergence speed. Finally, to verify the performance of SAOHTC. In this paper, two benchmark experiments of IEEE CEC2014 and IEEE CEC2017 and five engineering problems are used to test the superior performance of SAOHTC.
{"title":"Improved Snow Ablation Optimizer with Heat Transfer and Condensation Strategy for Global Optimization Problem","authors":"Heming Jia, Fangkai You, Di Wu, Honghua Rao, Hangqu Wu, Laith Abualigah","doi":"10.1093/jcde/qwad096","DOIUrl":"https://doi.org/10.1093/jcde/qwad096","url":null,"abstract":"Abstract The Snow Ablation Optimizer (SAO) is a new metaheuristic algorithm proposed in April 2023. It simulates the phenomenon of snow sublimation and melting in nature and has a good optimization effect. The SAO proposes a new two-population mechanism. By introducing Brownian motion to simulate the random motion of gas molecules in space. However, as the temperature factor changes, most water molecules are converted into water vapor. Which breaks the balance between exploration and exploitation, and reduces the optimization ability of the algorithm in the later stage. Especially in the face of high-dimensional problems, it is easy to fall into local optimal. In order to improve the efficiency of the algorithm, this paper proposes an improved Snow Ablation Optimizer with Heat Transfer and Condensation Strategy(SAOHTC). Firstly, this article proposes a heat transfer strategy. Utilizes gas molecules to transfer heat from high to low temperatures and move their positions from low to high temperatures. Causing individuals with lower fitness in the population to move towards individuals with higher fitness, thereby improving the optimization efficiency of the original algorithm. Secondly, a condensation strategy is proposed. Which can transform water vapor into water by simulating condensation in nature, improve the deficiency of the original two-population mechanism. improve the convergence speed. Finally, to verify the performance of SAOHTC. In this paper, two benchmark experiments of IEEE CEC2014 and IEEE CEC2017 and five engineering problems are used to test the superior performance of SAOHTC.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136376322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (1) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half waits beneath to catch it; (2) Coatis avoidance predators behavior. Which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm's performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coatis optimization algorithm (ICOA) to enhance the algorithm's efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm's exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm's global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems.
{"title":"Improve Coati Optimization Algorithm for Solving Constrained Engineering Optimization Problems","authors":"Heming Jia, Shengzhao Shi, Di Wu, Honghua Rao, Jinrui Zhang, Laith Abualigah","doi":"10.1093/jcde/qwad095","DOIUrl":"https://doi.org/10.1093/jcde/qwad095","url":null,"abstract":"Abstract The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (1) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half waits beneath to catch it; (2) Coatis avoidance predators behavior. Which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm's performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coatis optimization algorithm (ICOA) to enhance the algorithm's efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm's exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm's global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"83 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jie Xing, Qinqin Zhao, Huiling Cheny, Yili Zhang, Feng Zhou, Hanli Zhao
Abstract We present a Bee Foraging Behavior Driven Mutational Salp Swarm Algorithm (BMSSA) based on an improved bee foraging strategy and an unscented mutation strategy. The improved bee foraging strategy is leveraged in the follower location update phase to break the fixed range search of SSA, while the unscented mutation strategy on the optimal solution is employed to enhance the quality of the optimal solution. Extensive experimental results on public CEC 2014 benchmark functions validate that the proposed BMSSA performs better than nine well-known metaheuristic methods and seven state-of-the-art algorithms. The Binary BMSSA algorithm is further proposed for feature selection by using BMSSA as the selection strategy and support vector machine as the classifier. Experimental comparisons on twelve UCI datasets demonstrate the superiority of binary BMSSA. Finally, we collected a dataset on the return-intentions of overseas Chinese after COVID-19 through an anonymous online questionnaire and performed a case study by setting up a binary BMSSA-based feature selection optimization model. . The case study shows that the development prospects, the family and job in the place of residence, seeking opportunities in China, and the possible time to return to China are critical factors influencing the willingness to return to China after COVID-19.
{"title":"Utilizing Bee Foraging Behavior in Mutational Salp Swarm for Feature Selection: A Study on Return Intentions of Overseas Chinese after COVID-19","authors":"Jie Xing, Qinqin Zhao, Huiling Cheny, Yili Zhang, Feng Zhou, Hanli Zhao","doi":"10.1093/jcde/qwad092","DOIUrl":"https://doi.org/10.1093/jcde/qwad092","url":null,"abstract":"Abstract We present a Bee Foraging Behavior Driven Mutational Salp Swarm Algorithm (BMSSA) based on an improved bee foraging strategy and an unscented mutation strategy. The improved bee foraging strategy is leveraged in the follower location update phase to break the fixed range search of SSA, while the unscented mutation strategy on the optimal solution is employed to enhance the quality of the optimal solution. Extensive experimental results on public CEC 2014 benchmark functions validate that the proposed BMSSA performs better than nine well-known metaheuristic methods and seven state-of-the-art algorithms. The Binary BMSSA algorithm is further proposed for feature selection by using BMSSA as the selection strategy and support vector machine as the classifier. Experimental comparisons on twelve UCI datasets demonstrate the superiority of binary BMSSA. Finally, we collected a dataset on the return-intentions of overseas Chinese after COVID-19 through an anonymous online questionnaire and performed a case study by setting up a binary BMSSA-based feature selection optimization model. . The case study shows that the development prospects, the family and job in the place of residence, seeking opportunities in China, and the possible time to return to China are critical factors influencing the willingness to return to China after COVID-19.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135779843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations – low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance.
{"title":"Sand Cat Arithmetic Optimization Algorithm for Global Optimization Engineering Design Problems","authors":"Shuilin Chen, Jianguo Zheng","doi":"10.1093/jcde/qwad094","DOIUrl":"https://doi.org/10.1093/jcde/qwad094","url":null,"abstract":"Abstract Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations – low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136034173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yunlou Qian, Jiaqing Tu, Gang Luo, Ce Sha, Ali Asghar Heidari, Huiling Chen
Abstract Remote sensing images can provide direct and accurate feedback on urban surface morphology and geographic conditions. They can be used as an auxiliary means to collect data for current geospatial information systems, which are also widely used in city public safety. Therefore, it is necessary to research remote-sensing images. Therefore, we adopt the multi-threshold image segmentation method in this paper to segment the remote-sensing images for research. We first introduce salp foraging behavior into the continuous ant colony optimization algorithm (ACOR) and construct a novel ACOR version based on salp foraging (SSACO). The original algorithm's convergence and ability to avoid hitting local optima are enhanced by salp foraging behavior. In order to illustrate this key benefit, SSACO is first put up against 14 fundamental algorithms using 30 benchmark test functions in IEEE CEC2017. Then, SSACO is compared against 14 other algorithms. The experimental results are examined from various angles, and the findings convincingly demonstrate the main selling point of SSACO. We performed segmentation comparison studies based on 12 remote sensing images between SSACO segmentation techniques and several peer segmentation approaches to demonstrate the benefits of SSACO in remote sensing image segmentation. Peak signal-to-noise ratio, structural similarity index, and feature similarity index evaluation of the segmentation results demonstrated the benefits of the SSACO-based segmentation approach. SSACO is an excellent optimizer since it seeks to serve as a guide and a point of reference for using remote sensing image algorithms in urban public safety.
{"title":"Multi-threshold remote sensing image segmentation with improved ant colony optimizer with salp foraging","authors":"Yunlou Qian, Jiaqing Tu, Gang Luo, Ce Sha, Ali Asghar Heidari, Huiling Chen","doi":"10.1093/jcde/qwad093","DOIUrl":"https://doi.org/10.1093/jcde/qwad093","url":null,"abstract":"Abstract Remote sensing images can provide direct and accurate feedback on urban surface morphology and geographic conditions. They can be used as an auxiliary means to collect data for current geospatial information systems, which are also widely used in city public safety. Therefore, it is necessary to research remote-sensing images. Therefore, we adopt the multi-threshold image segmentation method in this paper to segment the remote-sensing images for research. We first introduce salp foraging behavior into the continuous ant colony optimization algorithm (ACOR) and construct a novel ACOR version based on salp foraging (SSACO). The original algorithm's convergence and ability to avoid hitting local optima are enhanced by salp foraging behavior. In order to illustrate this key benefit, SSACO is first put up against 14 fundamental algorithms using 30 benchmark test functions in IEEE CEC2017. Then, SSACO is compared against 14 other algorithms. The experimental results are examined from various angles, and the findings convincingly demonstrate the main selling point of SSACO. We performed segmentation comparison studies based on 12 remote sensing images between SSACO segmentation techniques and several peer segmentation approaches to demonstrate the benefits of SSACO in remote sensing image segmentation. Peak signal-to-noise ratio, structural similarity index, and feature similarity index evaluation of the segmentation results demonstrated the benefits of the SSACO-based segmentation approach. SSACO is an excellent optimizer since it seeks to serve as a guide and a point of reference for using remote sensing image algorithms in urban public safety.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136079375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xingbin Chen, Sining Li, Gengying Li, Bin Xue, Bingsheng Liu, Yuan Fang, Seo JoonOh, Inhan Kim, Jung In Kim
Abstract Applying BIM and VR in construction education is an effective way to achieve better study motivation, learnability, creativity, and observation of the real world. However, whether different levels of BIM prior knowledge affect students’ VR experimental learning, if at all, has not been examined. Therefore, this study employs a teaching intervention experiment to access the VR learning process based on the BIM prior knowledge. A total of 47 students, from the Department of Architecture and Civil Engineering, City University of Hong Kong, participated in the experiment. They were grouped according to whether they had taken the prior BIM tutorial section, with twenty-three participants in the group having completed the tutorial and twenty-four participants in the group that had not. Experiment materials were created and rendered via Autodesk Revit and Iris VR; the materials supported three tasks related to the underground design review scenarios and three other tasks about site planning review scenarios. After the experiment, a comparison study was done to discuss their differences based on VR task performances and satisfaction. The results revealed that the BIM prior knowledge mediated both the two-dimension and three-dimension navigations when students performed the tasks. Moreover, the relationship differences within the satisfactions showed that BIM prior knowledge effectively affected the learning outcomes. In conclusion, the comparison study implies that students’ BIM prior knowledge is efficacious in the students’ VR task performance and their VR satisfaction from cognitive and memory perspectives.
{"title":"Effects of BIM prior knowledge on applying VR in construction education: Lessons from a comparison study","authors":"Xingbin Chen, Sining Li, Gengying Li, Bin Xue, Bingsheng Liu, Yuan Fang, Seo JoonOh, Inhan Kim, Jung In Kim","doi":"10.1093/jcde/qwad091","DOIUrl":"https://doi.org/10.1093/jcde/qwad091","url":null,"abstract":"Abstract Applying BIM and VR in construction education is an effective way to achieve better study motivation, learnability, creativity, and observation of the real world. However, whether different levels of BIM prior knowledge affect students’ VR experimental learning, if at all, has not been examined. Therefore, this study employs a teaching intervention experiment to access the VR learning process based on the BIM prior knowledge. A total of 47 students, from the Department of Architecture and Civil Engineering, City University of Hong Kong, participated in the experiment. They were grouped according to whether they had taken the prior BIM tutorial section, with twenty-three participants in the group having completed the tutorial and twenty-four participants in the group that had not. Experiment materials were created and rendered via Autodesk Revit and Iris VR; the materials supported three tasks related to the underground design review scenarios and three other tasks about site planning review scenarios. After the experiment, a comparison study was done to discuss their differences based on VR task performances and satisfaction. The results revealed that the BIM prior knowledge mediated both the two-dimension and three-dimension navigations when students performed the tasks. Moreover, the relationship differences within the satisfactions showed that BIM prior knowledge effectively affected the learning outcomes. In conclusion, the comparison study implies that students’ BIM prior knowledge is efficacious in the students’ VR task performance and their VR satisfaction from cognitive and memory perspectives.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136015591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zafar Hayat Khan, O D Makinde, M Usman, R Ahmad, W A Khan, Zaitang Huang
Abstract This study focuses on fractional order derivatives for the unsteady flow of MHD methanol-iron oxide nanofluid over a permeable vertical plate. The study incorporates fractional order derivatives to exhibit the system mathematically. The concluding model, which consists of the system of PDEs, has been solved via the Finite Difference Method, and graphical illustrations demonstrate the effects of key parameters on the flow field. Velocity and temperature profiles provide insights into nanofluid behavior. Additionally, essential quantities such as skin friction coefficient, Nusselt number, Bejan number, and entropy generation rate have been depicted graphically. Comparison with previous studies authenticates the accuracy of the anticipated model, contributing to new intuitions into MHD nanofluid flow over a permeable vertical plate. It is worth noting that the current model, incorporating fractional order derivatives, contributes to understanding the physical characteristics of MHD CH3OH-Fe3O4 nanofluid flow over a permeable vertical plate, research that has not been extensively explored before.
{"title":"Inherent Irreversibility in Unsteady MHD Nanofluid Flow Past a Slippery Permeable Vertical Plate with Fractional Order Derivative","authors":"Zafar Hayat Khan, O D Makinde, M Usman, R Ahmad, W A Khan, Zaitang Huang","doi":"10.1093/jcde/qwad090","DOIUrl":"https://doi.org/10.1093/jcde/qwad090","url":null,"abstract":"Abstract This study focuses on fractional order derivatives for the unsteady flow of MHD methanol-iron oxide nanofluid over a permeable vertical plate. The study incorporates fractional order derivatives to exhibit the system mathematically. The concluding model, which consists of the system of PDEs, has been solved via the Finite Difference Method, and graphical illustrations demonstrate the effects of key parameters on the flow field. Velocity and temperature profiles provide insights into nanofluid behavior. Additionally, essential quantities such as skin friction coefficient, Nusselt number, Bejan number, and entropy generation rate have been depicted graphically. Comparison with previous studies authenticates the accuracy of the anticipated model, contributing to new intuitions into MHD nanofluid flow over a permeable vertical plate. It is worth noting that the current model, incorporating fractional order derivatives, contributes to understanding the physical characteristics of MHD CH3OH-Fe3O4 nanofluid flow over a permeable vertical plate, research that has not been extensively explored before.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The Beluga Whale Optimization(BWO) Algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization(MBWO) with a multi-strategy. It was inspired by beluga whales' two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group gathering strategy (GAs) and a migration strategies (Ms). The group aggregation strategy can improve the local development ability of the algorithm and accelerate the overall Rate of convergence through the group aggregation fine search; The migration strategy randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO's ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.
{"title":"Modified Beluga Whale Optimization with Multi-strategies for Solving Engineering Problems","authors":"Heming Jia, Qixian Wen, Di Wu, Zhuo Wang, Yuhao Wang, Changsheng Wen, Laith Abualigah","doi":"10.1093/jcde/qwad089","DOIUrl":"https://doi.org/10.1093/jcde/qwad089","url":null,"abstract":"Abstract The Beluga Whale Optimization(BWO) Algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization(MBWO) with a multi-strategy. It was inspired by beluga whales' two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group gathering strategy (GAs) and a migration strategies (Ms). The group aggregation strategy can improve the local development ability of the algorithm and accelerate the overall Rate of convergence through the group aggregation fine search; The migration strategy randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO's ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Buket SAMANCI, Özge TAŞPINAR, Yaşar Emir KARCI, Başak CENGİZ, Selen OZDOGAN, Dilek YILDIZ, Michael Stefan BİTTERMANN
Preferences for using physical mock-up modeling or computer-aided design (CAD) among architecture students in the early design phase are analyzed. The data is obtained from a questionnaire, consisting of eight multiple-choice questions and one open-ended question. The respondents are architecture students; the majority of them are still in their undergraduate studies. As quantitative analysis methods hypothesis tests based on the probability distributions known as the z-distribution, and the Chi-squared distribution were carried out. Generally, it was investigated which modeling technique is more efficient in the early design phase. Moreover, according to the age groups of respondents, the difference in the preference among mock-up and CAD is identified. Explicitly, younger students prefer CAD, while other ones prefer mock-up representation. The reasons for the difference are analyzed. Since the choice for mock-up modeling or CAD modeling can have a strong impact on the design processes of both, students and professionals, the result of the study is relevant, because it gives a hint about probable future architecture practice.
{"title":"Erken Tasarım Aşamasında Mimarlık Öğrencilerinin Fiziksel ve Bilgisayar Destekli Modelleme Kullanım Tercihleri","authors":"Buket SAMANCI, Özge TAŞPINAR, Yaşar Emir KARCI, Başak CENGİZ, Selen OZDOGAN, Dilek YILDIZ, Michael Stefan BİTTERMANN","doi":"10.53710/jcode.1307294","DOIUrl":"https://doi.org/10.53710/jcode.1307294","url":null,"abstract":"Preferences for using physical mock-up modeling or computer-aided design (CAD) among architecture students in the early design phase are analyzed. The data is obtained from a questionnaire, consisting of eight multiple-choice questions and one open-ended question. The respondents are architecture students; the majority of them are still in their undergraduate studies. As quantitative analysis methods hypothesis tests based on the probability distributions known as the z-distribution, and the Chi-squared distribution were carried out. Generally, it was investigated which modeling technique is more efficient in the early design phase. Moreover, according to the age groups of respondents, the difference in the preference among mock-up and CAD is identified. Explicitly, younger students prefer CAD, while other ones prefer mock-up representation. The reasons for the difference are analyzed. Since the choice for mock-up modeling or CAD modeling can have a strong impact on the design processes of both, students and professionals, the result of the study is relevant, because it gives a hint about probable future architecture practice.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136278008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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