A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody s...A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.展开更多
Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by co...Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.展开更多
Air film conveyors equipped with porous pads have been developed to bring the liquid crystal display(LCD) into a non-contact state during transportation process. In this work, a theoretical model including flow proper...Air film conveyors equipped with porous pads have been developed to bring the liquid crystal display(LCD) into a non-contact state during transportation process. In this work, a theoretical model including flow property of porous media and Reynolds equation is established within a representative region in order to optimize the design parameters of a partial porous air conveyor. With the theoretical model, an optimization method using nondominated sorting genetic algorithm – II(NSGA-II) is applied for a two-objective optimization to achieve a minimum air consumption and maximum load capacity. Three Pareto-optimal solutions are selected to analyze the influence of each parameter on the characteristics of the air conveyor, and the results indicate that the position of the porous pads has the most significant impact on the performance and of course must be determined with care. Furthermore, experimental results in terms of the supporting force versus gap clearance show that the optimized air conveyor can greatly improve the load capacity over the normal one, indicating that the optimization method is applicable for practical use.展开更多
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProjects(20040533035, 20070533131) supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.
文摘Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.
基金Project(51205174)supported by the National Natural Science Foundation of ChinaProject(2014M550309)supported by the Postdoctoral Science Foundation of ChinaProject(GZKF-201407)supported by the Open Foundation of the State Key Laboratory of Fluid Power Transmission and Control,China
文摘Air film conveyors equipped with porous pads have been developed to bring the liquid crystal display(LCD) into a non-contact state during transportation process. In this work, a theoretical model including flow property of porous media and Reynolds equation is established within a representative region in order to optimize the design parameters of a partial porous air conveyor. With the theoretical model, an optimization method using nondominated sorting genetic algorithm – II(NSGA-II) is applied for a two-objective optimization to achieve a minimum air consumption and maximum load capacity. Three Pareto-optimal solutions are selected to analyze the influence of each parameter on the characteristics of the air conveyor, and the results indicate that the position of the porous pads has the most significant impact on the performance and of course must be determined with care. Furthermore, experimental results in terms of the supporting force versus gap clearance show that the optimized air conveyor can greatly improve the load capacity over the normal one, indicating that the optimization method is applicable for practical use.
