The Application Of Improved Adaptive Genetic Algorithm In Structure Of Trusses Optimization

The purpose of structure optimization is to make some evaluation indicators (weight, stiffness, cost, etc) of a designed structure which meet the various specific requirements or constraints achieve to the best. The optimization method which can determine the best schemes for engineering design by combining optimization theory with computer technique has been widely used in the design field. As a novel and powerful intelligent optimization technology, bionics method has more advantages over traditional optimization methods when solving problems with global search, complex design domain and complicated target functions, and it is easier to use. Genetic Algorithm (GA) which has been the hot research work in the world in last years is one of the most important algorithms in bionics method. Global optimization, adaptive and connotative parallel searching is one of its most important characteristics.In this paper, some modifications on Simple Genetic Algorithm (SGA) were proposed based on the late study and research on the basic principles, design concept of GA and properties of truss. An improved GA can solve the defects such as poor local search optimization ability, premature convergence, and difficulty to determine parameters and so on of SGA. In this paper, we use the Improved Adaptive Genetic Algorithm (IAGA) to resolve the optimization of Truss structure. The main research work in this paper is as follows:1. Introduced the basic principles and operations of SGA. Introduced the conversion ways of fitness function and the commonly used methods of dealing with constraints in GA; Analyzed the defects of SGA such as poor local search ability, premature convergence, the best individual of every generation failed to protect, punish factor has a great impact on result and so on.2. Some modifications on SGA were proposed based on the defects of SGA and properties of truss. Use the appropriate coding technique; Chose the correct fitness function; Improved the crossover operator and mutation operator in the SGA; Proposed the adaptive crossover operator and mutation operator which can change dynamically with the evolution of population. This improvement can improve the efficiency of optimization and enhance convergence. The new method proposed the elitist strategy in order to overcome the defects that the best individual of every generation failed to protect in SGA.3. The improved new adaptive genetic algorithm presented in this paper is programmed using MATLAB. Finally, optimize the structure of three bar truss and ten bar truss based on the idea of new algorithm and the optimal results and courses of the new algorithm are compared with that of simple genetic algorithm show that the improved adaptive genetic algorithm is feasible and effective.