In operations research, the transportation problem (TP) is among the earliest and most effective applications of the linear programming problem. Unbalanced transportation problems reflect the reality of supply chain a...In operations research, the transportation problem (TP) is among the earliest and most effective applications of the linear programming problem. Unbalanced transportation problems reflect the reality of supply chain and logistics situations where the available supply of goods may not precisely match the demand at different locations. To deal with an unbalanced transportation problem (UTP), it is essential first to convert it into a balanced transportation problem (BTP) to find an initial basic feasible solution (IBFS) and hence the optimal solution. The present paper is concerned with introducing a new approach to convert an unbalanced transportation problem into a balanced one and as a consequence to obtain optimum total transportation cost. Numerical examples are provided to demonstrate the suggested method.展开更多
With the acceleration of urbanization,the construction industry has developed rapidly worldwide but has also brought serious environmental problems.Traditional architectural design methods often only focus on the func...With the acceleration of urbanization,the construction industry has developed rapidly worldwide but has also brought serious environmental problems.Traditional architectural design methods often only focus on the function and beauty of the building while ignoring its impact on the environment.In addition,the lack of effective design and construction management methods also led to high resource and energy consumption.To overcome this challenge,the concept of green building came into being.Green buildings emphasize reducing the negative impact of buildings on the environment and improving resource utilization efficiency throughout the entire life cycle.BIM technology provides strong support for achieving this goal.Based on this,starting from the role of BIM technology in green building performance optimization,this article analyzes the optimization of green building performance solutions based on BIM technology in detail to promote the sustainable development of buildings.展开更多
The optimization problem is considered in which the objective function is pseudolinear(both pseudoconvex and pseudoconcave) and the constraints are linear. The general expression for the optimal solutions to the pro...The optimization problem is considered in which the objective function is pseudolinear(both pseudoconvex and pseudoconcave) and the constraints are linear. The general expression for the optimal solutions to the problem is derived with the representation theorem of polyhedral sets, and the uniqueness condition of the optimal solution and the computational procedures to determine all optimal solutions (if the uniqueness condition is not satisfied ) are provided. Finally, an illustrative example is also given.展开更多
In this paper, we have used two reliable approaches (theorems) to find the optimal solutions to transportation problems, using variations in costs. In real-life scenarios, transportation costs can fluctuate due to dif...In this paper, we have used two reliable approaches (theorems) to find the optimal solutions to transportation problems, using variations in costs. In real-life scenarios, transportation costs can fluctuate due to different factors. Finding optimal solutions to the transportation problem in the context of variations in cost is vital for ensuring cost efficiency, resource allocation, customer satisfaction, competitive advantage, environmental responsibility, risk mitigation, and operational fortitude in practical situations. This paper opens up new directions for the solution of transportation problems by introducing two key theorems. By using these theorems, we can develop an algorithm for identifying the optimal solution attributes and permitting accurate quantification of changes in overall transportation costs through the addition or subtraction of constants to specific rows or columns, as well as multiplication by constants inside the cost matrix. It is anticipated that the two reliable techniques presented in this study will provide theoretical insights and practical solutions to enhance the efficiency and cost-effectiveness of transportation systems. Finally, numerical illustrations are presented to verify the proposed approaches.展开更多
In this paper, the nonlinear programming problem with quasimonotonic ( both quasiconvex and quasiconcave )objective function and linear constraints is considered. With the decomposition theorem of polyhedral sets, t...In this paper, the nonlinear programming problem with quasimonotonic ( both quasiconvex and quasiconcave )objective function and linear constraints is considered. With the decomposition theorem of polyhedral sets, the structure of optimal solution set for the programming problem is depicted. Based on a simplified version of the convex simplex method, the uniqueness condition of optimal solution and the computational procedures to determine all optimal solutions are given, if the uniqueness condition is not satisfied. An illustrative example is also presented.展开更多
A new method to solve dynamic nonlinear constrained optimization problems (DNCOP) is proposed. First, the time (environment) variable period of DNCOP is divided into several equal subperiods. In each subperiod, th...A new method to solve dynamic nonlinear constrained optimization problems (DNCOP) is proposed. First, the time (environment) variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem (SNCOP). Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into a static bi-objective optimization problem. As a result, the original DNCOP is approximately transformed into several static bi-objective optimization problems. Third, a new multiobjective evolutionary algorithm is proposed based on a new selection operator and an improved nonuniformity mutation operator. The simulation results indicate that the proposed algorithm is effective for DNCOP.展开更多
The traditional foundry industry has developed rapidly in recently years due to advancements in computer technology. Modifying and designing the feeding system has become more convenient with the help of the casting s...The traditional foundry industry has developed rapidly in recently years due to advancements in computer technology. Modifying and designing the feeding system has become more convenient with the help of the casting software, Inte CAST. A common method of designing a feeding system is to first design the initial systems, run simulations with casting software, analyze the feedback, and then redesign. In this work, genetic, fruit fly, and interior point optimizer(IPOPT) algorithms were introduced to guide the optimal riser design for the feeding system. The results calculated by the three optimal algorithms indicate that the riser volume has a weak relationship with the modulus constraint; while it has a close relationship with the volume constraint. Based on the convergence rate, the fruit fly algorithm was obviously faster than the genetic algorithm. The optimized riser was also applied during casting, and was simulated using Inte CAST. The numerical simulation results reveal that with the same riser volume, the riser optimized by the genetic and fruit fly algorithms has a similar improvement on casting shrinkage. The IPOPT algorithm has the advantage of causing the smallest shrinkage porosities, compared to those of the genetic and fruit fly algorithms, which were almost the same.展开更多
As a new-style stochastic algorithm, the electromagnetism-like mechanism(EM) method gains more and more attention from many researchers in recent years. A novel model based on EM(NMEM) for multiobjective optimizat...As a new-style stochastic algorithm, the electromagnetism-like mechanism(EM) method gains more and more attention from many researchers in recent years. A novel model based on EM(NMEM) for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.展开更多
In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce ...In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual's relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify 'good' individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.展开更多
Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization(PSO). The very basic PSO model and some variants of PSO do not consider the enhancement of...Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization(PSO). The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO(ECE-PSO) is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.展开更多
To properly describe and solve complex decision problems, research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important. We establish in this paper different Lipschitz-...To properly describe and solve complex decision problems, research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important. We establish in this paper different Lipschitz-type continuity results about the optimal value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of the linear constraints. The obtained results extend some existing results for continuous quadratic programs, and, more importantly, lay the foundation for further theoretical study and corresponding algorithm analysis on mixed-integer quadratic programs.展开更多
With the expression theorem of convex polyhedron, this paper gives the general expression for the solutions in standard linear programming problems. And the calculation procedures in determining the optimal solutions ...With the expression theorem of convex polyhedron, this paper gives the general expression for the solutions in standard linear programming problems. And the calculation procedures in determining the optimal solutions are also given.展开更多
We propose a new unified path to approximately smoothing the nonsmooth exact penalty function in this paper. Based on the new smooth penalty function, we give a penalty algorithm to solve the constrained optimization ...We propose a new unified path to approximately smoothing the nonsmooth exact penalty function in this paper. Based on the new smooth penalty function, we give a penalty algorithm to solve the constrained optimization problem, and discuss the convergence of the algorithm under mild conditions.展开更多
In Dynamic Economic Load Dispatch (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Opt...In Dynamic Economic Load Dispatch (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are used to encode in a vector form and in sharing information and both approaches are based on the master-apprentice mechanism for the Dual Evolution Strategy. In order to overcome the challenges like the clustering of PSO, optimization problems and maximum and minimum searching, a new approach is developed with the improvement of searching and efficient process. In this paper, an Enhanced Hybrid Differential Evolution and Particle Swarm Optimization (EHDE-PSO) is proposed with Dynamic Sigmoid Weight using parallel procedures. A hybrid form of the proposed approach combines the optimizing algorithm of Enhanced PSO with the Differential Evolution (DE) for the improvement of computation using parallel process. The implementation and the parallel process are analyzed and discussed to gather relevant data to show the performance enhancement which is better than the existing algorithm.展开更多
Spacecraft pose estimation is an important technology to maintain or change the spacecraft orientation in space.For spacecraft pose estimation,when two spacecraft are relatively distant,the depth information of the sp...Spacecraft pose estimation is an important technology to maintain or change the spacecraft orientation in space.For spacecraft pose estimation,when two spacecraft are relatively distant,the depth information of the space point is less than that of the measuring distance,so the camera model can be seen as a weak perspective projection model.In this paper,a spacecraft pose estimation algorithm based on four symmetrical points of the spacecraft outline is proposed.The analytical solution of the spacecraft pose is obtained by solving the weak perspective projection model,which can satisfy the requirements of the measurement model when the measurement distance is long.The optimal solution is obtained from the weak perspective projection model to the perspective projection model,which can meet the measurement requirements when the measuring distance is small.The simulation results show that the proposed algorithm can obtain better results,even though the noise is large.展开更多
With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, curr...With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.展开更多
A quadratic bilevel programming problem is transformed into a single level complementarity slackness problem by applying Karush-Kuhn-Tucker(KKT) conditions.To cope with the complementarity constraints,a binary encod...A quadratic bilevel programming problem is transformed into a single level complementarity slackness problem by applying Karush-Kuhn-Tucker(KKT) conditions.To cope with the complementarity constraints,a binary encoding scheme is adopted for KKT multipliers,and then the complementarity slackness problem is simplified to successive quadratic programming problems,which can be solved by many algorithms available.Based on 0-1 binary encoding,an orthogonal genetic algorithm,in which the orthogonal experimental design with both two-level orthogonal array and factor analysis is used as crossover operator,is proposed.Numerical experiments on 10 benchmark examples show that the orthogonal genetic algorithm can find global optimal solutions of quadratic bilevel programming problems with high accuracy in a small number of iterations.展开更多
With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network tr...With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network transmission has led to low data processing efficiency.Fortunately,edge computing can solve this problem,effectively reduce the delay of data transmission,and improve data processing capacity,so that the crowdsourcing platform can make better decisions faster.Therefore,this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment(MOO-TA)problem in the edge computing environment.The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area.In this paper,the Weighted and Multi-Objective Particle Swarm Combination(WAMOPSC)algorithm is proposed to maximize both platform’s and crowd workers’utility,so as to maximize social welfare.The algorithm combines the traditional Linear Weighted Summation(LWS)algorithm and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose.Through comparison experiments on real data sets,the effectiveness and feasibility of the proposed method are evaluated.展开更多
In this paper,weak optimal inverse problems of interval linear programming(IvLP)are studied based on KKT conditions.Firstly,the problem is precisely defined.Specifically,by adjusting the minimum change of the current ...In this paper,weak optimal inverse problems of interval linear programming(IvLP)are studied based on KKT conditions.Firstly,the problem is precisely defined.Specifically,by adjusting the minimum change of the current cost coefficient,a given weak solution can become optimal.Then,an equivalent characterization of weak optimal inverse IvLP problems is obtained.Finally,the problem is simplified without adjusting the cost coefficient of null variable.展开更多
文摘In operations research, the transportation problem (TP) is among the earliest and most effective applications of the linear programming problem. Unbalanced transportation problems reflect the reality of supply chain and logistics situations where the available supply of goods may not precisely match the demand at different locations. To deal with an unbalanced transportation problem (UTP), it is essential first to convert it into a balanced transportation problem (BTP) to find an initial basic feasible solution (IBFS) and hence the optimal solution. The present paper is concerned with introducing a new approach to convert an unbalanced transportation problem into a balanced one and as a consequence to obtain optimum total transportation cost. Numerical examples are provided to demonstrate the suggested method.
文摘With the acceleration of urbanization,the construction industry has developed rapidly worldwide but has also brought serious environmental problems.Traditional architectural design methods often only focus on the function and beauty of the building while ignoring its impact on the environment.In addition,the lack of effective design and construction management methods also led to high resource and energy consumption.To overcome this challenge,the concept of green building came into being.Green buildings emphasize reducing the negative impact of buildings on the environment and improving resource utilization efficiency throughout the entire life cycle.BIM technology provides strong support for achieving this goal.Based on this,starting from the role of BIM technology in green building performance optimization,this article analyzes the optimization of green building performance solutions based on BIM technology in detail to promote the sustainable development of buildings.
文摘The optimization problem is considered in which the objective function is pseudolinear(both pseudoconvex and pseudoconcave) and the constraints are linear. The general expression for the optimal solutions to the problem is derived with the representation theorem of polyhedral sets, and the uniqueness condition of the optimal solution and the computational procedures to determine all optimal solutions (if the uniqueness condition is not satisfied ) are provided. Finally, an illustrative example is also given.
文摘In this paper, we have used two reliable approaches (theorems) to find the optimal solutions to transportation problems, using variations in costs. In real-life scenarios, transportation costs can fluctuate due to different factors. Finding optimal solutions to the transportation problem in the context of variations in cost is vital for ensuring cost efficiency, resource allocation, customer satisfaction, competitive advantage, environmental responsibility, risk mitigation, and operational fortitude in practical situations. This paper opens up new directions for the solution of transportation problems by introducing two key theorems. By using these theorems, we can develop an algorithm for identifying the optimal solution attributes and permitting accurate quantification of changes in overall transportation costs through the addition or subtraction of constants to specific rows or columns, as well as multiplication by constants inside the cost matrix. It is anticipated that the two reliable techniques presented in this study will provide theoretical insights and practical solutions to enhance the efficiency and cost-effectiveness of transportation systems. Finally, numerical illustrations are presented to verify the proposed approaches.
基金Supported by the Research Foundation of Jinan University(04SKZD01).
文摘In this paper, the nonlinear programming problem with quasimonotonic ( both quasiconvex and quasiconcave )objective function and linear constraints is considered. With the decomposition theorem of polyhedral sets, the structure of optimal solution set for the programming problem is depicted. Based on a simplified version of the convex simplex method, the uniqueness condition of optimal solution and the computational procedures to determine all optimal solutions are given, if the uniqueness condition is not satisfied. An illustrative example is also presented.
基金supported by the National Natural Science Foundation of China (60374063)the Natural Science Basic Research Plan Project in Shaanxi Province (2006A12)+1 种基金the Science and Technology Research Project of the Educational Department in Shaanxi Province (07JK180)the Emphasis Research Plan Project of Baoji University of Arts and Science (ZK0840)
文摘A new method to solve dynamic nonlinear constrained optimization problems (DNCOP) is proposed. First, the time (environment) variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem (SNCOP). Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into a static bi-objective optimization problem. As a result, the original DNCOP is approximately transformed into several static bi-objective optimization problems. Third, a new multiobjective evolutionary algorithm is proposed based on a new selection operator and an improved nonuniformity mutation operator. The simulation results indicate that the proposed algorithm is effective for DNCOP.
基金financially supported by the National Science and Technology Key Projects of Numerical Control(2012ZX04012-011)the State Key Laboratory of Materials Processing and Die&Mold Technology Research Project(2014,2015)
文摘The traditional foundry industry has developed rapidly in recently years due to advancements in computer technology. Modifying and designing the feeding system has become more convenient with the help of the casting software, Inte CAST. A common method of designing a feeding system is to first design the initial systems, run simulations with casting software, analyze the feedback, and then redesign. In this work, genetic, fruit fly, and interior point optimizer(IPOPT) algorithms were introduced to guide the optimal riser design for the feeding system. The results calculated by the three optimal algorithms indicate that the riser volume has a weak relationship with the modulus constraint; while it has a close relationship with the volume constraint. Based on the convergence rate, the fruit fly algorithm was obviously faster than the genetic algorithm. The optimized riser was also applied during casting, and was simulated using Inte CAST. The numerical simulation results reveal that with the same riser volume, the riser optimized by the genetic and fruit fly algorithms has a similar improvement on casting shrinkage. The IPOPT algorithm has the advantage of causing the smallest shrinkage porosities, compared to those of the genetic and fruit fly algorithms, which were almost the same.
基金supported by the National Natural Science Foundation of China(60873099)the Fundamental Research Funds for the Central Universities(2011QNA29)
文摘As a new-style stochastic algorithm, the electromagnetism-like mechanism(EM) method gains more and more attention from many researchers in recent years. A novel model based on EM(NMEM) for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.
基金supported by the National Natural Science Foundation of China(No.60803049,60472060)
文摘In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual's relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify 'good' individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions.
基金supported by the Aeronautical Science Fund of Shaanxi Province of China(20145596025)
文摘Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization(PSO). The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO(ECE-PSO) is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.
基金Supported by the National Natural Science Foundation of China(10571141,70971109)the Key Projectof the National Natural Science Foundation of China(70531030)
文摘To properly describe and solve complex decision problems, research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important. We establish in this paper different Lipschitz-type continuity results about the optimal value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of the linear constraints. The obtained results extend some existing results for continuous quadratic programs, and, more importantly, lay the foundation for further theoretical study and corresponding algorithm analysis on mixed-integer quadratic programs.
文摘With the expression theorem of convex polyhedron, this paper gives the general expression for the solutions in standard linear programming problems. And the calculation procedures in determining the optimal solutions are also given.
文摘We propose a new unified path to approximately smoothing the nonsmooth exact penalty function in this paper. Based on the new smooth penalty function, we give a penalty algorithm to solve the constrained optimization problem, and discuss the convergence of the algorithm under mild conditions.
文摘In Dynamic Economic Load Dispatch (DELD), optimization and evolution computation become a major part with the strategy for solving the issues. From various algorithms Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are used to encode in a vector form and in sharing information and both approaches are based on the master-apprentice mechanism for the Dual Evolution Strategy. In order to overcome the challenges like the clustering of PSO, optimization problems and maximum and minimum searching, a new approach is developed with the improvement of searching and efficient process. In this paper, an Enhanced Hybrid Differential Evolution and Particle Swarm Optimization (EHDE-PSO) is proposed with Dynamic Sigmoid Weight using parallel procedures. A hybrid form of the proposed approach combines the optimizing algorithm of Enhanced PSO with the Differential Evolution (DE) for the improvement of computation using parallel process. The implementation and the parallel process are analyzed and discussed to gather relevant data to show the performance enhancement which is better than the existing algorithm.
基金Supported by National Natural Science Foundation of China(Grant No.12272104).
文摘Spacecraft pose estimation is an important technology to maintain or change the spacecraft orientation in space.For spacecraft pose estimation,when two spacecraft are relatively distant,the depth information of the space point is less than that of the measuring distance,so the camera model can be seen as a weak perspective projection model.In this paper,a spacecraft pose estimation algorithm based on four symmetrical points of the spacecraft outline is proposed.The analytical solution of the spacecraft pose is obtained by solving the weak perspective projection model,which can satisfy the requirements of the measurement model when the measurement distance is long.The optimal solution is obtained from the weak perspective projection model to the perspective projection model,which can meet the measurement requirements when the measuring distance is small.The simulation results show that the proposed algorithm can obtain better results,even though the noise is large.
文摘With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.
基金supported by the National Natural Science Foundation of China (60873099)
文摘A quadratic bilevel programming problem is transformed into a single level complementarity slackness problem by applying Karush-Kuhn-Tucker(KKT) conditions.To cope with the complementarity constraints,a binary encoding scheme is adopted for KKT multipliers,and then the complementarity slackness problem is simplified to successive quadratic programming problems,which can be solved by many algorithms available.Based on 0-1 binary encoding,an orthogonal genetic algorithm,in which the orthogonal experimental design with both two-level orthogonal array and factor analysis is used as crossover operator,is proposed.Numerical experiments on 10 benchmark examples show that the orthogonal genetic algorithm can find global optimal solutions of quadratic bilevel programming problems with high accuracy in a small number of iterations.
基金supported in part by the National Natural Science Foundation of China under Grant 61822602,Grant 61772207,Grant 61802331,Grant 61572418,Grant 61602399,Grant 61702439 and Grant 61773331the China Postdoctoral Science Foundation under Grant 2019T120732 and Grant 2017M622691+1 种基金the National Science Foundation(NSF)under Grant 1704287,Grant 1252292 and Grant 1741277the Natural Science Foundation of Shandong Province under Grant ZR2016FM42.
文摘With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network transmission has led to low data processing efficiency.Fortunately,edge computing can solve this problem,effectively reduce the delay of data transmission,and improve data processing capacity,so that the crowdsourcing platform can make better decisions faster.Therefore,this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment(MOO-TA)problem in the edge computing environment.The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area.In this paper,the Weighted and Multi-Objective Particle Swarm Combination(WAMOPSC)algorithm is proposed to maximize both platform’s and crowd workers’utility,so as to maximize social welfare.The algorithm combines the traditional Linear Weighted Summation(LWS)algorithm and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose.Through comparison experiments on real data sets,the effectiveness and feasibility of the proposed method are evaluated.
基金Supported by the National Natural Science Foundation of China(11971433)First Class Discipline of Zhe-jiang-A(Zhejiang Gongshang University-Statistics,1020JYN4120004G-091),Graduate Scientic Research and Innovation Foundation of Zhejiang Gongshang University.
文摘In this paper,weak optimal inverse problems of interval linear programming(IvLP)are studied based on KKT conditions.Firstly,the problem is precisely defined.Specifically,by adjusting the minimum change of the current cost coefficient,a given weak solution can become optimal.Then,an equivalent characterization of weak optimal inverse IvLP problems is obtained.Finally,the problem is simplified without adjusting the cost coefficient of null variable.