We present a new definition (Evolving Solutions) for Multi-objective Optimization Problem (MOP) to answer the basic question (what's multi-objective optimal solution?) and advance an asynchronous evolutionary mode...We present a new definition (Evolving Solutions) for Multi-objective Optimization Problem (MOP) to answer the basic question (what's multi-objective optimal solution?) and advance an asynchronous evolutionary model (MINT Model) to solve MOPs. The new theory is based on our understanding of the natural evolution and the analysis of the difference between natural evolution and MOP, thus it is not only different from the Converting Optimization but also different from Pareto Optimization. Some tests prove that our new theory may conquer disadvantages of the upper two methods to some extent.展开更多
This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sli...This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.展开更多
A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta...A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.展开更多
<div style="text-align:justify;"> In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics ...<div style="text-align:justify;"> In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm. </div>展开更多
This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Auto...This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.展开更多
Based on one-dimensional water quality model and nonlinear programming, the point source pollution reduction model with multi-objective optimization has been established. To achieve cost effective and best water quali...Based on one-dimensional water quality model and nonlinear programming, the point source pollution reduction model with multi-objective optimization has been established. To achieve cost effective and best water quality, for us to optimize the process, we set pollutant concentration and total amount control as constraints and put forward the optimal pollution reduction control strategy by simulating and optimizing water quality monitoring data from the target section. Integrated with scenario analysis, COD and ammonia nitrogen pollution optimization wasstudiedin objective function area from Mountain Maan of Acheng to Fuerjia Bridge along Ashe River. The results showed that COD and NH3-N contribution has been greatly reduced to AsheRiverby 49.6% and 32.7% respectively. Therefore, multi-objective optimization by nonlinear programming for water pollution control can make source sewage optimization fairly and reasonably, and the optimal strategies of pollution emission are presented.展开更多
With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to th...With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.展开更多
To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with...To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability.展开更多
This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters...This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulink software, a simulation model is achieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems.展开更多
随着建筑行业对环境可持续性的日益重视,大型建筑基坑支撑结构设计中的碳排放问题已成为一个不容忽视的关键问题.传统设计虽然强调成本效益,但往往缺乏对环境性能的考虑.提出了一种基于建筑信息模型(building information modeling, BIM...随着建筑行业对环境可持续性的日益重视,大型建筑基坑支撑结构设计中的碳排放问题已成为一个不容忽视的关键问题.传统设计虽然强调成本效益,但往往缺乏对环境性能的考虑.提出了一种基于建筑信息模型(building information modeling, BIM)的增强约束方法,并将其集成至NSGA-Ⅱ框架,以实现基坑支护结构设计的多目标优化.首先,构建了基坑支护体系的碳排放计算准则,明确了计算的边界条件(涵盖建材的生产、运输、施工、拆除及回收等生命周期阶段);其次,在此基础上提出了一种增强约束方法,将设计规范转化为定量约束条件,并与NSGA-Ⅱ算法相结合,通过BIM平台提取结构信息,提升优化过程的精确性和可操作性;最后,针对2个典型基坑支护案例进行分析,以验证该方法的有效性和适用性.通过对2个基坑支护案例的研究分析可知,与传统设计相比该算法在成本和碳排放方面有明显改善,其设计在成本优化及碳性能优化设计的效率方面分别提高了40.9%和30.1%、25.3%和20.9%,为基坑支护结构设计提供了更加科学有效的优化方法.展开更多
Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro...Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.展开更多
基金Supported by the National Natural Science Foundation of China(70071042,60073043,60133010)
文摘We present a new definition (Evolving Solutions) for Multi-objective Optimization Problem (MOP) to answer the basic question (what's multi-objective optimal solution?) and advance an asynchronous evolutionary model (MINT Model) to solve MOPs. The new theory is based on our understanding of the natural evolution and the analysis of the difference between natural evolution and MOP, thus it is not only different from the Converting Optimization but also different from Pareto Optimization. Some tests prove that our new theory may conquer disadvantages of the upper two methods to some extent.
基金Supported by the National Natural Science Foundation of China(No.11172197 and No.11332008)a key-project grant from the Natural Science Foundation of Tianjin(No.010413595)
文摘This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.
基金supported by the National Natural Science Foundation of China(Grant 11572134)
文摘A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.
文摘<div style="text-align:justify;"> In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm. </div>
文摘This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.
文摘Based on one-dimensional water quality model and nonlinear programming, the point source pollution reduction model with multi-objective optimization has been established. To achieve cost effective and best water quality, for us to optimize the process, we set pollutant concentration and total amount control as constraints and put forward the optimal pollution reduction control strategy by simulating and optimizing water quality monitoring data from the target section. Integrated with scenario analysis, COD and ammonia nitrogen pollution optimization wasstudiedin objective function area from Mountain Maan of Acheng to Fuerjia Bridge along Ashe River. The results showed that COD and NH3-N contribution has been greatly reduced to AsheRiverby 49.6% and 32.7% respectively. Therefore, multi-objective optimization by nonlinear programming for water pollution control can make source sewage optimization fairly and reasonably, and the optimal strategies of pollution emission are presented.
文摘With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.
文摘To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability.
文摘This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulink software, a simulation model is achieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems.
文摘随着建筑行业对环境可持续性的日益重视,大型建筑基坑支撑结构设计中的碳排放问题已成为一个不容忽视的关键问题.传统设计虽然强调成本效益,但往往缺乏对环境性能的考虑.提出了一种基于建筑信息模型(building information modeling, BIM)的增强约束方法,并将其集成至NSGA-Ⅱ框架,以实现基坑支护结构设计的多目标优化.首先,构建了基坑支护体系的碳排放计算准则,明确了计算的边界条件(涵盖建材的生产、运输、施工、拆除及回收等生命周期阶段);其次,在此基础上提出了一种增强约束方法,将设计规范转化为定量约束条件,并与NSGA-Ⅱ算法相结合,通过BIM平台提取结构信息,提升优化过程的精确性和可操作性;最后,针对2个典型基坑支护案例进行分析,以验证该方法的有效性和适用性.通过对2个基坑支护案例的研究分析可知,与传统设计相比该算法在成本和碳排放方面有明显改善,其设计在成本优化及碳性能优化设计的效率方面分别提高了40.9%和30.1%、25.3%和20.9%,为基坑支护结构设计提供了更加科学有效的优化方法.
基金founded by the Doctoral Programs Foundation of the Ministry of Education of China (20130008110021)the National Natural Science Foundation of China (91425302, 41271536)International Science and Technology Cooperation Program of China (2013DFG70990)
文摘Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.
