Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many r...Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.展开更多
The author affiliation and the funding information in the Acknowledgement section of the online version of the original article was revised.One affiliation(the 8th affiliation)of the first author is added.The Acknowle...The author affiliation and the funding information in the Acknowledgement section of the online version of the original article was revised.One affiliation(the 8th affiliation)of the first author is added.The Acknowledgement section of the original article has been revised to:Acknowledgments:This research was funded by the National University of Mongolia under grant agreement P2023(grant number P2023-4578)and supported by the Chey Institute for Advanced Studies“International Scholarship Exchange Fellowship for the academic year of 2024-2025”,Republic of Korea,and the National University of Mongolia.We would like to acknowledge the National University of Mongolia and Soumik Das from the Center for the Study of Regional Development,Jawaharlal Nehru University,New Delhi-110067,for his valuable assistance in preparing the geological maps.展开更多
Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple p...Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.展开更多
At the late stage of solidification with ultrasonic treatment （UST） in Al-Si alloys, a part of semisolid overflows and climbs along the probe. The interesting phenomenon and its influence on the solidification micro...At the late stage of solidification with ultrasonic treatment （UST） in Al-Si alloys, a part of semisolid overflows and climbs along the probe. The interesting phenomenon and its influence on the solidification microstructure were investigated in order to better study the mechanism of UST. It is considered that the overflowing phenomenon occurs due to the changes of vibration and flow in the remaining semisolid. Because the overflowed portion comes from the region with intense UST effect and vibrates with the probe during solidification, great modification of primary and euteetic Si （about 10 pm in length） and refinement of primary a（Al） （about 70 μm in size） are observed in this portion.展开更多
文摘Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.
文摘The author affiliation and the funding information in the Acknowledgement section of the online version of the original article was revised.One affiliation(the 8th affiliation)of the first author is added.The Acknowledgement section of the original article has been revised to:Acknowledgments:This research was funded by the National University of Mongolia under grant agreement P2023(grant number P2023-4578)and supported by the Chey Institute for Advanced Studies“International Scholarship Exchange Fellowship for the academic year of 2024-2025”,Republic of Korea,and the National University of Mongolia.We would like to acknowledge the National University of Mongolia and Soumik Das from the Center for the Study of Regional Development,Jawaharlal Nehru University,New Delhi-110067,for his valuable assistance in preparing the geological maps.
基金supported by the National Natural Science Foun-dation of China(52325001,52170009,and 52400114)the National Key Research and Development Program of China(2021YFC3200700 and 2021YFC3200702)+1 种基金the Program of Shanghai Academic Research Leader,China(21XD1424000)the Fundamental Research Funds for the Central Universities.
文摘Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.
基金Project(50874022)supported by the National Natural Science Foundation of China
文摘At the late stage of solidification with ultrasonic treatment （UST） in Al-Si alloys, a part of semisolid overflows and climbs along the probe. The interesting phenomenon and its influence on the solidification microstructure were investigated in order to better study the mechanism of UST. It is considered that the overflowing phenomenon occurs due to the changes of vibration and flow in the remaining semisolid. Because the overflowed portion comes from the region with intense UST effect and vibrates with the probe during solidification, great modification of primary and euteetic Si （about 10 pm in length） and refinement of primary a（Al） （about 70 μm in size） are observed in this portion.
