According to the disadvantage of slow convergence rate of the basic differential evolution(DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead(NM) simplex method into the basic DE algorithm i...
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According to the disadvantage of slow convergence rate of the basic differential evolution(DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead(NM) simplex method into the basic DE algorithm is presented in this *** hybrid procedure performed the exploration with DE and the exploitation with the NM simplex *** to the control parameters of the proposed approach is *** computational results on several classical Benchmarks nonlinear complex functions show that the hybrid optimization algorithm is superior to the two original search techniques(*** and DE) in terms of solution quality and convergence *** with other DE variants,the proposed algorithm has better convergence performance and *** Wilcoxon non-parametric statistical tests also confirm the above claims.
In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for ...
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In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for that problem is *** an hybrid optimization algorithm combining bifuzzy simulation,genetic algorithm,and neural network is proposed to solve the *** the end of this paper,an numerical example is given to illustrate the feasibility of the proposed method.
In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for ...
详细信息
In this paper,net present value is taken into account while discussing multiproduct aggregate production planning decision making problems in bifuzzy ***,an expected value programming model with net present value for that problem is *** an hybrid optimization algorithm combining bifuzzy simulation,genetic algorithm,and neural network is proposed to solve the *** the end of this paper,an numerical example is given to illustrate the feasibility of the proposed method.
In this paper,a new framework for estimation of myocardium constitutive parameters is established.A more realistic,cardiac magnetic resonance image based realistic human left ventricular finite element analysis model ...
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In this paper,a new framework for estimation of myocardium constitutive parameters is established.A more realistic,cardiac magnetic resonance image based realistic human left ventricular finite element analysis model is introduced for analyzing the deformation of left ventricle during diastole firstly,the material behavior is described by the anisotropic nonlinear Holzapfel-Ogden constitutive model;then a novel hybrid simplex and quantum behavioral particle swarm optimizationalgorithm which is proposed to estimate the constitutive model parameters of myocardium as the inverse problem of left ventricle *** examples show that finite element analysis results and the estimated parameters are in good agreement with the experimental data reported in literature,which demonstrate that parameter estimation framework is *** with current optimizationalgorithm,the presented hybrid optimal algorithm can estimate the mechanical parameters more efficiently.
According to the disadvantage of slow convergence rate of the basic differential evolution (DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the basic DE algorithm...
详细信息
According to the disadvantage of slow convergence rate of the basic differential evolution (DE) algorithm, a hybrid optimization algorithm incorporated Nelder & Mead (NM) simplex method into the basic DE algorithm is presented in this paper. This hybrid procedure performed the exploration with DE and the exploitation with the NM simplex method. Sensitivity to the control parameters of the proposed approach is analyzed. The computational results on several classical Benchmarks nonlinear complex functions show that the hybrid optimization algorithm is superior to the two original search techniques (i.e. NM and DE) in terms of solution quality and convergence rate. Compared with other DE variants, the proposed algorithm has better convergence performance and robustness. The Wilcoxon non-parametric statistical tests also *** the above claims.
This paper investigates the operational research in active debris removal and focuses on the orbit control in multi debris removal. Far-range trajectory is the crucial phase which guides chaser to proximity of target ...
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This paper investigates the operational research in active debris removal and focuses on the orbit control in multi debris removal. Far-range trajectory is the crucial phase which guides chaser to proximity of target debris in sequence and provides precondition for further rendezvous control. Far-range trajectory can be divided into two modes depending on velocity requirement at the engagement instant. A novel mode, fly-by trajectory, is firstly proposed for far-range trajectory control corresponding to debris capture by compact with whipple material. Compared with general rendezvous and capture mode, there is no requirement on relative velocity in fly-by mode. At last, hybrid optimization algorithm is applied to far-range trajectory optimization based on multi-revolution Lambert algorithm. Simulations are conducted with three scenarios to illustrate effectiveness of proposed concept. This paper summarizes relevant work in this field and presents our new research in far-range orbit control.
Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A uniq...
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Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A unique parameterless hybrid is presented to explicitly evaluate ELD for test systems and real-world power plant systems matching the operational limitations. In addition, earlier algorithms could only offer estimates of the final cost of fuel based on the hyperparameter choices. This may prevent the global minimum values from being met. To find comprehensive solutions to the ELD problem in power systems, this paper suggests a new method called the hybrid Jaya optimizationalgorithm, which uses the merits of the Jaya and teaching-learning-based optimization (TLBO) algorithms. This enhancement is proposed to improve the population variety, the balance between local and global search, and the early convergence of the original Jaya optimization method. A metaheuristic optimization technique called TLBO simulates the teaching-learning process in a classroom to optimize problems. The TLBO algorithm uses an exploration phase in which possible solutions are generated at random to discover the best solution. The algorithm then uses the exploitation phase to refine the search space-based parameter adjustments to enhance the quality of the best solution identified. On the other hand, the Jaya algorithm is a metaheuristic optimizationalgorithm motivated by the idea of social behavior in nature. Candidate solutions are improved repeatedly through cooperation and competition using a population-based approach, and each solution adjusts its position based on the best and worst answers in the population. By combining the advantages of both algorithms, hybrid Jaya (Jaya-TLBO) outperforms each method alone and minimizes the cost of power generation, improving convergence solution quality. To test its efficacy, the hybrid Jaya-TLBO algorithm is tested on four different test c
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