Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which c...Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which can be both costly and time-consuming.This is especially true for large-scale transportation networks,where the size of the problem and the high computational cost can hinder the algorithm’s performance.To address these challenges,recent research has focused on using surrogate-assisted models.These models aim to reduce the number of expensive evaluations and improve the efficiency of solving time-consuming optimization problems.This paper presents a new two-layer Surrogate-Assisted Fish Migration Optimization(SA-FMO)algorithm designed to tackle high-dimensional and computationally heavy problems.The global surrogate model offers a good approximation of the entire problem space,while the local surrogate model focuses on refining the solution near the current best option,improving local optimization.To test the effectiveness of the SA-FMO algorithm,we first conduct experiments using six benchmark functions in a 50-dimensional space.We then apply the algorithm to optimize urban rail transit routes,focusing on the Train Routing Optimization problem.This aims to improve operational efficiency and vehicle turnover in situations with uneven passenger flow during transit disruptions.The results show that SA-FMO can effectively improve optimization outcomes in complex transportation scenarios.展开更多
Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely c...Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.展开更多
Multi-manned assembly line,which is broadly utilized to assemble high volume products such as automobiles and trucks,allows a group of workers to assemble different tasks simultaneously in a multi-manned workstation.T...Multi-manned assembly line,which is broadly utilized to assemble high volume products such as automobiles and trucks,allows a group of workers to assemble different tasks simultaneously in a multi-manned workstation.This additional characteristic of parallel operators increases the complexity of the traditional NP-hard assembly line balancing problem.Hence,this paper formulates the Type-I multi-manned assembly line balancing problem to minimize the total number of workstations and operators,and develops an efficient migrating birds optimization algorithm embedded into an idle time reduction method.In this algorithm,a new decoding mechanism is proposed which reduces the sequence-dependent idle time by some task assignment rules;three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases;and temperature acceptance and competitive mechanism are employed to avoid being trapped in the local optimum.Comparison experiments suggest that the new decoding and improvements are effective and the proposed algorithm outperforms the compared algorithms.展开更多
FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FS...FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.展开更多
Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER hav...Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.展开更多
Migratory birds rely on fueling prior to migratory flights. Fueling in migrants is controlled by intrinsic as well as ex- trinsic factors. From captive studies we have started understanding the internal mechanisms con...Migratory birds rely on fueling prior to migratory flights. Fueling in migrants is controlled by intrinsic as well as ex- trinsic factors. From captive studies we have started understanding the internal mechanisms controlling bird migration. Field studies have demonstrated the effects of external factors, such as food availability, weather, competitors, parasites or diseases, on the stopover behavior of migrants. However, an integrated approach is still missing to study coherently how the innate migration program interacts with the varying environmental cues and to estimate the contribution of the innate migration program and the environment to realized migration. The northern wheatear Oenanthe oenanthe offers a unique opportunity for integrated studies. It breeds across almost the whole Holarctic with just a ＂gap＂ between eastern Canada and Alaska. All breeding populations over- winter in sub-Saharan Africa which makes the northern wheatear one of the most long-distant migratory songbirds with extraor- dinary long non-stop flights across oceans. It is a nocturnal migrant which travels without parental or social aid/guidance. Thus, young birds rely entirely on endogenous mechanisms of timing, route selection and fueling on their first outbound migration. By establishing indoor housing under controlled conditions the endogenous control mechanisms of northern wheatear migration could be revealed. At the same time, environmental factors controlling fueling could be investigated in the field. On migration wheatears occur in a variety of habitats with sparse vegetation where their stopover behavior could be quantitatively studied in the light of ＂optimal migration＂ theory by the use of remote balances, radio-tagging and even experimentally manipulated food availability. The present paper summarizes our approach to understand the control of migration in northern wheatears by combin- ing field and laboratory studies at various spatial and temporal scales, and linking various sub-disciplines .展开更多
基金supported by the National Natural Science Foundation of China(Project No.52172321,52102391)Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)+1 种基金China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which can be both costly and time-consuming.This is especially true for large-scale transportation networks,where the size of the problem and the high computational cost can hinder the algorithm’s performance.To address these challenges,recent research has focused on using surrogate-assisted models.These models aim to reduce the number of expensive evaluations and improve the efficiency of solving time-consuming optimization problems.This paper presents a new two-layer Surrogate-Assisted Fish Migration Optimization(SA-FMO)algorithm designed to tackle high-dimensional and computationally heavy problems.The global surrogate model offers a good approximation of the entire problem space,while the local surrogate model focuses on refining the solution near the current best option,improving local optimization.To test the effectiveness of the SA-FMO algorithm,we first conduct experiments using six benchmark functions in a 50-dimensional space.We then apply the algorithm to optimize urban rail transit routes,focusing on the Train Routing Optimization problem.This aims to improve operational efficiency and vehicle turnover in situations with uneven passenger flow during transit disruptions.The results show that SA-FMO can effectively improve optimization outcomes in complex transportation scenarios.
文摘Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.
基金supported by the National Natural Science Foundation of China(51875421,61803287).
文摘Multi-manned assembly line,which is broadly utilized to assemble high volume products such as automobiles and trucks,allows a group of workers to assemble different tasks simultaneously in a multi-manned workstation.This additional characteristic of parallel operators increases the complexity of the traditional NP-hard assembly line balancing problem.Hence,this paper formulates the Type-I multi-manned assembly line balancing problem to minimize the total number of workstations and operators,and develops an efficient migrating birds optimization algorithm embedded into an idle time reduction method.In this algorithm,a new decoding mechanism is proposed which reduces the sequence-dependent idle time by some task assignment rules;three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases;and temperature acceptance and competitive mechanism are employed to avoid being trapped in the local optimum.Comparison experiments suggest that the new decoding and improvements are effective and the proposed algorithm outperforms the compared algorithms.
基金supported by Scientific Research Project of Necmettin Erbakan University
文摘FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.
文摘Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images.
文摘Migratory birds rely on fueling prior to migratory flights. Fueling in migrants is controlled by intrinsic as well as ex- trinsic factors. From captive studies we have started understanding the internal mechanisms controlling bird migration. Field studies have demonstrated the effects of external factors, such as food availability, weather, competitors, parasites or diseases, on the stopover behavior of migrants. However, an integrated approach is still missing to study coherently how the innate migration program interacts with the varying environmental cues and to estimate the contribution of the innate migration program and the environment to realized migration. The northern wheatear Oenanthe oenanthe offers a unique opportunity for integrated studies. It breeds across almost the whole Holarctic with just a ＂gap＂ between eastern Canada and Alaska. All breeding populations over- winter in sub-Saharan Africa which makes the northern wheatear one of the most long-distant migratory songbirds with extraor- dinary long non-stop flights across oceans. It is a nocturnal migrant which travels without parental or social aid/guidance. Thus, young birds rely entirely on endogenous mechanisms of timing, route selection and fueling on their first outbound migration. By establishing indoor housing under controlled conditions the endogenous control mechanisms of northern wheatear migration could be revealed. At the same time, environmental factors controlling fueling could be investigated in the field. On migration wheatears occur in a variety of habitats with sparse vegetation where their stopover behavior could be quantitatively studied in the light of ＂optimal migration＂ theory by the use of remote balances, radio-tagging and even experimentally manipulated food availability. The present paper summarizes our approach to understand the control of migration in northern wheatears by combin- ing field and laboratory studies at various spatial and temporal scales, and linking various sub-disciplines .
