Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm opt...Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.展开更多
The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an...The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.展开更多
The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selec...The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selected by the uniform design, and the inlet performance is analyzed by computational fluid dynamics （CFD）. Then complete quadratic polynomial response surface approximation models are constructed based on the performance analysis results and then used to replace theoriginal complex inlet performance model. The optimization is conducted using a multi-objective genetic algorithm NSGA-Ⅱ, and the Pareto optimal solution set is obtained. Results show that the uniform design and RSM can reduce the computational complexity of numerical simulation and improve the optimization efficiency.展开更多
In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objectiv...In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.展开更多
For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs)....For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms.展开更多
In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a N...In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.展开更多
文摘Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.
基金Supported by the National Basic Research Program of China ("973" Program)the National Natural Science Foundation of China (60872112, 10805012)+1 种基金the Natural Science Foundation of Zhejiang Province(Z207588)the College Science Research Project of Anhui Province (KJ2008B268)~~
文摘The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.
文摘The uniform design and response surface methodology （RSM） are applied to the multi-objective optimization of a 2-D mixed compression scramjet inlet. The set of experimental design points on the design space is selected by the uniform design, and the inlet performance is analyzed by computational fluid dynamics （CFD）. Then complete quadratic polynomial response surface approximation models are constructed based on the performance analysis results and then used to replace theoriginal complex inlet performance model. The optimization is conducted using a multi-objective genetic algorithm NSGA-Ⅱ, and the Pareto optimal solution set is obtained. Results show that the uniform design and RSM can reduce the computational complexity of numerical simulation and improve the optimization efficiency.
文摘In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.
文摘For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms.
基金supported by the Major National Science & Technology Specific Project of China under Grants No.2010ZX03002-007-02,No.2009ZX03002-002,No.2010ZX03002-002-03
文摘In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.
