Genetic Algorithm For Global Optimization Research Articles

PID Control Algorithm Based on Genetic Algorithm and its Application in Electric Cylinder Control

The traditional PID controller is simple in principle, easy to use, stable and reliable, and it is still widely used in the control field. However, for many nonlinear and lagging objects, the parameter tuning of PID controller is very important. Genetic algorithm provides a new way to optimize the parameters of PID. It uses simple coding techniques and propagation mechanisms to express complex phenomena, which is not restricted by the restriction of the search space. In this paper, the global optimization of genetic algorithm is used to optimize the parameters of PID, which can improve the performance and adaptive capability of PID controller. The mathematical model of the electric cylinder system is established, and the PID controller based on genetic algorithm is used to control the system. The simulation results verify the effectiveness of the proposed control algorithm.

Comparative Study on Prediction Effects of Short Fatigue Crack Propagation Rate by Two Different Calculation Methods

To describe the complicated nonlinear process of the fatigue short crack evolution behavior, especially the change of the crack propagation rate, two different calculation methods are applied. The dominant effective short fatigue crack propagation rates are calculated based on the replica fatigue short crack test with nine smooth funnel-shaped specimens and the observation of the replica films according to the effective short fatigue cracks principle. Due to the fast decay and the nonlinear approximation ability of wavelet analysis, the self-learning ability of neural network, and the macroscopic searching and global optimization of genetic algorithm, the genetic wavelet neural network can reflect the implicit complex nonlinear relationship when considering multi-influencing factors synthetically. The effective short fatigue cracks and the dominant effective short fatigue crack are simulated and compared by the Genetic Wavelet Neural Network. The simulation results show that Genetic Wavelet Neural Network is a rational and available method for studying the evolution behavior of fatigue short crack propagation rate. Meanwhile, a traditional data fitting method for a short crack growth model is also utilized for fitting the test data. It is reasonable and applicable for predicting the growth rate. Finally, the reason for the difference between the prediction effects by these two methods is interpreted.

Optimization Design of IGV Profile in Centrifugal Compressor

Variable inlet guide vane (IGV) is used to control the mass flow and generate prewhirl in centrifugal compressors. The efficient operation of IGV is limited to the range of aerodynamic characteristics of their vane profiles. In order to find out the best vane profile for IGV regulation, the modern optimization method was adopted to optimize the inlet guide vane profile. The main methodology idea was to use artificial neural network for continuous fitness evaluation and use genetic algorithm for global optimization. After optimization, the regulating performance of IGV has improved significantly, the prewhirl ability has been enhanced greatly, and the pressure loss has been reduced. The mass flow and power of compressor reduced by using the optimized guide vane at large setting angles, and the efficiency increased significantly; the flow field distribution has been improved obviously, since the nonuniform distribution of flow and flow separation phenomenon greatly weakened or even completely disappeared. The achievement of this research can effectively improve the regulation ability of IGV and the performance of compressor.

A novel adaptive genetic algorithm for global optimization of mathematical test functions and real-world problems

Genetic algorithm (GA) is a population-based stochastic optimization technique that has two major problems, i.e. low convergence speed and falling down in local optimum points. This paper introduces an adaptive genetic algorithm (AGA) consisting of new crossover and mutation operators to handle these drawbacks. The crossover operator is based on a combination of the traditional crossover mechanism and the particle swarm optimization (PSO) operator. The proposed mutation operator intelligently uses sliding mode control (SMC) to escape from local minimums and converges to the global optimum. The performance of the proposed genetic algorithm is challenged by using twenty well-known test functions. The comparison of the obtained numerical results with those of the other optimization algorithms reported in literature demonstrates the superiority of the proposed algorithm in finding the global optimum points. At the end, the proposed method is employed to estimate the oil demand in Iran based on socio-economic indicators and using linear and exponential forms as a real-world problem that shows the AGA’s effectiveness.

Integrated Optimization Design of Carbon Fiber Composite Framework for Small Lightweight Space Camera

A Carbon Fiber Composite (CFC) framework was designed for a small lightweight space camera. According to the distribution characteristics of each optical element in the optical system, CFC (M40J) was chosen to accomplish the design of the framework. TC4 embedded parts were used to solve the low accuracy of the CFC framework interface problem. An integrated optimization method and the optimization strategy which combined a genetic global optimization algorithm with a downhill simplex local optimization algorithm were adopted to optimize the structure parameters of the framework. After optimization, the total weight of the CFC framework and the TC4 embedded parts is 15.6 kg, accounting for only 18.4% that of the camera. The first order frequency of the camera reaches 104.8 Hz. Finally, a mechanical environment test was performed, and the result demonstrates that the first order frequency of the camera is 102 Hz, which is consistent with the simulation result. It further verifies the rationality and correctness of the optimization result. The integrated optimization method mentioned in this paper can be applied to the structure design of other space cameras, which can greatly improve the structure design efficiency.

Developing Clustering Based on Genetic Algorithm for Global Optimization

Developing Clustering Based on Genetic Algorithm for Global Optimization

Global optimization and oxygen dissociation on polyicosahedral Ag32Cu6 core-shell cluster for alkaline fuel cells

The structure of 38 atoms Ag-Cu cluster is studied by using a combination of a genetic algorithm global optimization technique and density functional theory (DFT) calculations. It is demonstrated that the truncated octahedral (TO) Ag32Cu6 core-shell cluster is less stable than the polyicosahedral (pIh) Ag32Cu6 core-shell cluster from the atomistic models and the DFT calculation shows an agreeable result, so the newfound pIh Ag32Cu6 core-shell cluster is further investigated for potential application for O2 dissociation in oxygen reduction reaction (ORR). The activation energy barrier for the O2 dissociation on pIh Ag32Cu6 core-shell cluster is 0.715 eV, where the d-band center is −3.395 eV and the density of states at the Fermi energy level is maximal for the favorable absorption site, indicating that the catalytic activity is attributed to a maximal charge transfer between an oxygen molecule and the pIh Ag32Cu6 core-shell cluster. This work revises the earlier idea that Ag32Cu6 core-shell nanoparticles are not suitable as ORR catalysts and confirms that Ag-Cu nanoalloy is a potential candidate to substitute noble Pt-based catalyst in alkaline fuel cells.

Optimized aperiodic multilayer structures for use as narrow-angular absorbers

In this paper, we investigate aperiodic multilayer structures for use as narrow-angular absorbers. The layer thicknesses and materials are optimized using a genetic global optimization algorithm coupled to a transfer matrix code to maximize the angular selectivity in the absorptance at a single or multiple wavelengths. We first consider structures composed of alternating layers of tungsten and silicon or silica, and find that it is not possible to achieve angular selectivity in the absorptance with such structures. We next consider structures composed of alternating layers of silicon and silica, and show that when optimized they exhibit high angular selectivity in absorptance. In addition, as the angular selectivity in absorptance increases, the wavelength range of high angular selectivity also decreases. Optimizing the material composition of the multilayer structures, in addition to optimizing the layer thicknesses, leads to marginal improvement in angular selectivity. Finally, we show that by optimizing the absorptance of the multilayer structures at multiple wavelengths, we can obtain structures exhibiting almost perfect absorptance at normal incidence and narrow angular width in absorptance at these wavelengths. Similar to the structures optimized at a single wavelength, the wavelength range of high angularly selective absorptance is narrow.

Small Solid Propellant Launch Vehicle Mixed Design Optimization Approach

For a small country with limited research budget and lack of advanced space technology, it is imperative to find new approaches for the development of low-cost launch vehicles (LV), which is, among all possibilities, an interesting option for rapid access to space, focused on integration of acquired components complemented with indigenously developed subsystems. This approach requires the cooperation of developed countries with huge experience and knowledge in LV development and operations. The main objective is to develop a small three stage solid propellant LV capable of delivering a payload of 100 kg to a circular low earth orbit of 600 km altitude, with the first and second stage solid rocket motors (SRM) hypothetically acquired from different countries and the third one designed and produced domestically in accordance with the production and technological capability. This approach provides main advantages such as: reduction in total time to access the space and to master the basic knowledge of launch operations. For this purpose, an integer continuous genetic algorithm global optimization method was selected and implemented, the SRM characteristics of the first and second stage were considered as integer variables, whereas the design variables of the third stage SRM and the trajectory variable were considered as continuous. A multi discipline feasible (MDF) framework was implemented along with the propulsion, aerodynamic, mass and trajectory models. Despite their particular characteristics and constraints, the results show highly acceptable values, and the approach proved to be reliable for conceptual design level.

Frequency up-conversion in nonpolar a-plane GaN/AlGaN based multiple quantum wells optimized for applications with silicon solar cells

We have described a method for structural parameters optimization of GaN/AlGaN multiple quantum well based up-converter for silicon solar cells. It involves a systematic tuning of individual step quantum wells by use of the genetic algorithm for global optimization. In quantum well structures, the up-conversion process can be achieved by utilizing nonlinear optical effects based on intersubband transitions. Both single and double step quantum wells have been tested in order to maximize the second order susceptibility derived from the density matrix formalism. The results obtained for single step wells proved slightly better and have been further pursued to obtain a more complex design, optimized for conversion of an entire range of incident photon energies.

Adaptive on-line Genetic Algorithm Based PD Iterative Control for PUMA560

In order to improve the accuracy of the repetitive motion trajectory tracking control for industrial robots of less rapid demanding, an online adaptive PD iterative learning control algorithm is proposed. In order to improve the accuracy, PD parameters are optimized online at each sampling time with the advantage of genetic algorithm for global optimization. In order to avoid overshoot, penalty function is used and overshoot is regard as one of the best indicators. Finally, this algorithm is tried in PUMA560. Simulation analysis shows that the proposed algorithm is better than unmodified in accuracy.

A New BP Algorithm and its Application

In this paper, based on the combination of Genetic algorithm and BP algorithm, a new algorithm is proposed in this paper. The BP operator is embedded in the genetic operation in the algorithm, the algorithm effectively assimilates the global optimization of genetic algorithm and fast convergence of BP algorithm, and it encodes the construction and the weights hybrid with real code and binary code, achieving the same step optimization of structure and weights. The simulation results show that, the new algorithm can quickly converge to the global optimal solution, but also can obtain the best approximation of weights in the network structure.

Optimal Launch Vehicle Configuration from Existing Solid Rocket Motors Using Genetic Algorithm

This paper describes the optimization approach of a three stage solid propellant launch vehicle configuration from existing solid rocket motors (SRM). The optimal launch vehicle (LV) is capable of delivering a small satellite of 100 kg to a circular low earth orbit of 400, 500 and 600 km altitude. The overall LV configuration and the trajectory profile were optimized simultaneously, thus the existing SRM parameters for first, second and three stages, vertical flight time, launch maneuver variable, maximum angle of attack, coasting time between first and second stage and the second coasting time between second and third stages were optimized. A genetic algorithm global optimization method has been implemented to perform the analysis, the algorithm consider mixed integer continuous variables. The results show that the proposed optimization approach was able to find the optimal solution for all three variants with very acceptable values, and the approach proved to be reliable for conceptual design level.

A new genetic algorithm for global optimization of multimodal continuous functions

In this paper a new genetic algorithm is developed to find the near global optimal solution of multimodal nonlinear optimization problems. The algorithm defined makes use of a real encoded crossover and mutation operator. The performance of GA is tested on a set of twenty-seven nonlinear global optimization test problems of variable difficulty level. Results are compared with some well established popular GAs existing in the literature. It is observed that the algorithm defined performs significantly better than the existing ones.

Wavelength Optimization for Quantitative Spectral Imaging of Breast Tumor Margins

A wavelength selection method that combines an inverse Monte Carlo model of reflectance and a genetic algorithm for global optimization was developed for the application of spectral imaging of breast tumor margins. The selection of wavelengths impacts system design in cost, size, and accuracy of tissue quantitation. The minimum number of wavelengths required for the accurate quantitation of tissue optical properties is 8, with diminishing gains for additional wavelengths. The resulting wavelength choices for the specific probe geometry used for the breast tumor margin spectral imaging application were tested in an independent pathology-confirmed ex vivo breast tissue data set and in tissue-mimicking phantoms. In breast tissue, the optical endpoints (hemoglobin, β-carotene, and scattering) that provide the contrast between normal and malignant tissue specimens are extracted with the optimized 8-wavelength set with <9% error compared to the full spectrum (450–600 nm). A multi-absorber liquid phantom study was also performed to show the improved extraction accuracy with optimization and without optimization. This technique for selecting wavelengths can be used for designing spectral imaging systems for other clinical applications.

A Directed Genetic Algorithm for global optimization

Within the framework of real-coded genetic algorithms, this paper proposes a directed genetic algorithm (DGA) that introduces a directed crossover operator and a directed mutation operator. The operation schemes of these operators borrow from the reflection and the expansion search mode of the Nelder–Mead’s simplex method. First, the Taguchi method is employed to study the influence analysis of the parameters in the DGA. The results show that the parameters in the DGA have strong robustness for solving the global optimal solution. Then, several strategies are proposed to enhance the solution accuracy capability of the DGA. All of the strategies are applied to a set of 30/100-dimensional benchmark functions to prove their superiority over several genetic algorithms. Finally, a cantilevered beam design problem with constrained conditions is used as a practical structural optimization example for demonstrating the very good performance of the proposed method.

Multi-Objective Flexible Job-Shop Scheduling Method Study Based on The Integration Algorithm of GA-BP

Aiming to the analysis of multi-varieties and small-batch job-shop scheduling problem, the BP neural network and genetic algorithm are analyzed. A multi-objective GA-BP algorithm is built using the combination of the global optimization of genetic algorithm with the local optimization of traditional BP network model. Based on the analysis on theory of the integration of GA-BP neural network, the integration algorithm of GA-BP neural network is realized in detail. An example of JobShop dynamic scheduling is performed. The simulation results verify the effective and feasible of GABP algorithm.

Main Beam Optimization of Wind Turbine Blade Base on Multi-Objective Genetic Algorithm

Take s814 airfoil as an example, established the multi-objective optimization model of moment of inertia and the weight for wind turbine blade main beam,Using the genetic algorithm global optimization algorithm, and given the Pareto solution set of optimal with the form of Pareto front. Select four kinds of optimization results scheme to do finite element calculation. The results shows that the magnitude of moment of inertia accordance with the change trend of main beam deflection, the width and thick of beam cap have great affect on moment of inertia and weight, the nearer aerodynamic center of leading edge, the greater moment of inertia, the thick of web almost no influence on moment of inertia.

DFT study of the structures and energetics of 98-atom AuPd clusters

The energetics, structures and segregation of 98-atom AuPd nanoclusters are investigated using a genetic algorithm global optimization technique with the Gupta empirical potential (comparing three different potential parameterisations) followed by local minimizations using Density Functional Theory (DFT) calculations. A shell optimization program algorithm is employed in order to study the energetics of the highly symmetric Leary Tetrahedron (LT) structure and optimization of the chemical ordering of a number of structural motifs is carried out using the Basin Hopping Monte Carlo approach. Although one of the empirical potentials is found to favour the LT structure, it is shown that Marks Decahedral and mixed FCC-HCP motifs are lowest in energy at the DFT level.

Hydraulic Optimization of Multiphase Pump Based on CFD and Genetic Algorithm

Impellers of helicon-axial multiphase pump are optimized based on CFD and genetic algorithm. The method mainly includes: CFD numerical calculation, to establish nonlinear relation through neural network, and genetic algorithm optimization extreme. Firstly, the profile of blades is parametric by spline surface and Choose 12 control points as optimization variables. Then, every optimization variable is given optimal dimension. Finally, sample database is got by using standard L27_3_13 orthogonal design table. Next, output values are got by modeling every sample, meshing generation and using CFD numerical calculation. Train neural networks through the database; thus the nonlinear relation between the blade parameter and pump performance parameters is built by applying the nonlinear fitting ability of BP neural networks. Regard the trained neural network as a fitness function of the genetic algorithm and use the characteristic of nonlinear global optimization of genetic algorithm to optimize the multiphase pump. Optimization result shows that the hydraulic efficiency of the multiphase pump is increased by 1.91%.