Ruizhen Gao

An Improved Parallel Adaptive Genetic Algorithm Based on Pareto Front for Multi-objective Problems

For multi-objective optimization problems, we introduced IPAGA (Improved Parallel Adaptive Genetic Algorithm) in this paper, a new parallel genetic algorithm which is based on Pareto Front. In this Algorithm, the non-dominated-set is constructed by the method of exclusion. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The results show that the parallel genetic algorithm developed in this paper is efficient.

An improved genetic algorithm based on J 1 triangulation and fixed point theory

An improved genetic algorithm based on J1 triangulation is proposed for multimodal optimization problems. And the fixed point theory is introduced into this improved algorithm. The optimal problems are conversed to fixed point problems. In this paper, several typical functions are used to demonstrate the effectiveness of this algorithm, and the testing results show that the improved genetic algorithm is valid and highly effective.

An Improved Multi-Objective Adaptive Niche Genetic Algorithm Based On Pareto Front

For multi-objective optimization problems, an improved multi-objective adaptive niche genetic algorithm based on Pareto Front is proposed in this paper. In this Algorithm, the rank value and the niche value are introduced to evaluate the individuals. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The experimental results show that this algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution.

A joint optimization method of genetic algorithm and numerical algorithm based on MATLAB

The mathematic model of a two-bar truss is built in MATLAB and the analysis is carried out by the genetic algorithm toolbox. In order to compare with each other, the parametric model of the planar truss is also established by the ANSYS Parametric Design Language and solutions are obtained using the first-order method native to ANSYS. The comparison of the results shows that genetic algorithms do not always display better properties than other algorithms for some problems. Finally, a joint optimization method which combines MATLAB genetic algorithm toolbox and the numerical algorithm based on the quasi-Newton method is proposed. Then the method is identified through the numerical example of the two-bar truss. The simulation results indicate that the joint optimization method can always converge to the global optimal solution.

An Improve Genetic Algorithm Based on Fixed Point Algorithms

An improved genetic algorithm is proposed to solve optimal problems, which is based on fixed point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on a simplicial subdivision of searching space and generates the integer labels at the vertices, then, applied crossover operators and increasing dimension operators according to these labels. In this case, it is used as an objective convergence criterion and termination criterion that the labels of every individual are completely labeled simplexes. The algorithm combines genetic algorithms with fixed point algorithms and triangulation theory to maintain the proper diversity, stability and convergence of the population. Several numerical examples are provided to be examined and the numerical results illustrate that the proposed algorithm has higher global optimization capability, computing efficiency and stronger stability than traditional numerical optimization methods and the standard genetic algorithm.

3-Degree-of-Freedom Parallel Robot Control Based Fuzzy Theory

Based on fuzzy control theory, a new method for 3- Degree-of-freedom (DOF) parallel robot control is presented in this paper. Take example for DELTA robot; input the established physical model in Pro/E into simulation software ADAMS to establish its mechanical system model by seamless interface software MECH/Pro, where DELTA robot can do kinematics and dynamics simulation. Build the fuzzy controller and control system model by the use of MATLAB software. Realize co-simulation in accordance with a given target trajectory by the use of ADAMS/MATLAB software. The results analysis shows that this control method has solved coordinate control for the 3-DOF parallel robot so that each output is fed back to the three drive rod, effectively improving the control precision, tracing trajectory in real time and providing a method of control for multi-DOF parallel robot.

An Improved Method of Structural Optimization Based on ANSYS

Because of the deficiency existing in both zero-order algorithm and first-order algorithm of ANSYS, this paper presents an improved method combining with both algorithms. Firstly, the zero-order algorithm is adopted to confirm the approximate location of the optimal solution, which will not be trapped into local optimum solutions. Secondly, the first-order algorithm is applied to determine the best solution by executing restart of optimization design. Finally, the improved method is examined through a numerical example by virtue of ANSYS10.0 and MATLAB7.0. The simulation results indicate that this method is more effective than traditional methods and advantages of it are much more remarkable when it is applied to complicated engineering structures.

An Improved Genetic Algorithm Based on Fixed Point Theory for Function Optimization

This paper introduces triangulation theory into genetic algorithm and with which, the optimization problem will be translated into a fixed point problem. An improved genetic algorithm is proposed by virtue of the concept of relative coordinates genetic coding, designs corresponding crossover and mutation operator. Through genetic algorithms to overcome the triangulation of the shortcomings of human grade, it can start from any point to find the most advantages. Gradually fine mesh will be introduced the idea of genetic algorithms so that the search area gradually decreased, improving the efficiency of search. Finally, examples demonstrate the effectiveness of this method.

Neural Network Predictive Control for Piezoelectric Smart Structures

The piezoelectric smart structure is a force-electric coupling structure, and piezoelectric patches can not be patched ideally, so it is difficult to build the accurate mathematical model of piezoelectric smart structure. The traditional vibration control methods depend on the structural mathematical model, and the control result is unsatisfactory. Considering this problem, this paper introduces the nonlinear generalized predictive control algorithm based on neural network predictive model into piezoelectric smart structure. Because of the difficulties of building the mathematical model and extracting dynamic data from experiment, the finite element software (ANSYS) is employed to analyze and obtain the dynamic response data of piezoelectric smart structure through modal analysis and transient analysis. Neural network predictive model of structure is built through off-line training on the basis of the data. The nonlinear generalized predictive control based on neural network has a better ability to solve complex nonlinear problem. Then the author introduces the Neural Network Based System Identification Toolbox (NNSYSID) and Neural Network Based Control System Design Toolkit (NNCTRL), which are two special toolboxes for designing neural network control system and can save lots of time for designers who can commit themselves to sixty-four-dollar question. At last, the author shows the method through a case. A cantilever beam which surface is boned piezoelectric patches used for sensor and actuator respectively is analyzed by ANSYS and controled by the neural network predictive control algorithm on the platform of NNSYSID and NNCTRL. This is a simple and effective method for designers to solve the vibration control problem of piezoelectric smart structure.Copyright

The application of co-simulation technology of ANSYS and MSC.ADAMS in structural engineering

The combination of ANSYS and ADAMS can improve the precision of system simulation. In this paper, first, a cable-stayed space truss, consisting of stay cables, columns, and the space truss, is built in ANSYS. Second, the modal neutral file (mnf) needed in ADAMS is generated by executing the macro command “ADAMS” and then imported into ADAMS software. Third, the flexible body in the mnf file is connected to the rigid body built in ADAMS through the external nodes specified in ANSYS, and then the dynamic analysis can be carried out. Subsequently, the load file needed by ANSYS could be obtained by clicking on the menu “File Export”. Finally, the load file is imported into ANSYS software by the APDL language and the static analyses corresponding to different load steps are carried out. The simulation analysis shows a good performance and some results we are interested in are obtained.