Optimization Process Knowledge-Based Genetic Optimization Algorithm for Excavator Stick

In view of the deficiencies existing in current knowledge-based global numerical optimization for excavator stick such as the insufficiency acquiring and utilizing optimization process knowledge to guide the global numerical optimization for stick, the easily falling in local optimal solution and so on, the optimization process knowledge-based genetic optimization algorithm for excavator stick is proposed. By establishing the selecting operator, crossover operator and mutation operator based on optimization process knowledge to realize the sufficiency in utilizing optimization process knowledge to guide the genetic intelligent optimization algorithm for stick. The structural optimization of ear-plate stick for medium hydraulic excavator is taken as example, which demonstrates that the genetic optimization algorithm based on optimization process knowledge for stick is feasible and efficacious. Introduction Lightweight and high strength are two important but contradictory targets for mechanical design [1], which is difficult to be coordinated for complex component. In recent years, the numerical optimization methods of structural intelligent optimization are mostly researched through the angle of the pure numerical optimization algorithm combined with analysis software [2,3], or the numerical optimization algorithm combined with fuzzy expert system [4], or the numerical optimization based on static knowledge [5,6]. However, using the existing method, the global numerical algorithm fails to sufficiently utilize optimization process knowledge and easily fall in local optimal solution in the process. For complex component, it is necessary to seek a more effective method to complete the structural intelligent optimization. Therefore, the optimization process knowledge based genetic algorithm for excavator stick is proposed in this paper. Intelligent Genetic Optimization Algorithm for Stick Aimed at improving the efficiency of global optimization of genetic algorithm, selecting operator, crossover operator and mutation operator based on optimization process knowledge and judgment operator of individual comprehensive state are established to construct optimization process knowledge based intelligent genetic optimization algorithm for stick. The main steps of the intelligent genetic optimization algorithm are shown in Fig.1 and illustrated as follows. Selection Operator Based on Optimization Process Knowledge To improve the efficiency of coordinating the lightweight and structural performance, a calculation formula of individual fitness value based on the weight distribution of weight and stress is established. Calculating the individual fitness value to realize the reasonable choice of the excellent individual. The optimization population can be divided into excellent feasible individuals, feasible individuals, excellent invalid individuals and invalid individuals, according to the individual fitness value. In order to preserve the diversity of individual as much as possible, the excellent feasible individuals can genetic to the next generation directly, the feasible individuals and invalid individuals can be used as parent matching cross individuals, the excellent invalid individuals can be used as parent mutants. Selection operator Optimization population Optimization process constraint knowledge Optimization process objective knowledge New optimization population Judgment operator Crossover operator Mutation operator Figure 1. Intelligent genetic optimization algorithm based on optimization process knowledge. Judgment Operator of Individual Comprehensive State The judgment operator is used to identify the stress state of individual each stress characteristic sections and extract the target sensitivity knowledge of individual each real number coding genes, to determine the expected search direction and reasonable adjustment step of parent individual genes. The stress state of stress characteristic section refers to the relationship between the maximum stress of stress characteristic section and the allowable stress of structural material under the multi-working conditions, and coding the stress state. The coding string composed of the stress state codes of each stress characteristic sections can effectively represent the individual stress state. The individual real number coding genes both are the global optimization variables. According to the coding string of parent individual stress state can query the expected search direction and reasonable adjustment step of each global optimization variables, so as to obtain the opcode of each parent individual genes. Crossover Operator Based on Optimization Process Knowledge In order to maximize the probability that offspring is better than the parent, establishing the crossover operator based on optimization process knowledge. Using the crossover operator can fully extract the parent individual objective knowledge and constraint knowledge to determine whether crossover operator is required of each genes of the parent cross-matching individual, and to determine the expected search direction and reasonable adjustment step of each genes. Mutation Operator Based on Optimization Process Knowledge In order to give full of each individual characteristics, establish the mutation operator based on optimization process knowledge to perform the different genes of mutants in different ways. After processing the geometry classification mode, the geometry of each individuals are reasonable. During the mutation operator, if the genes of individual geometry variables were mutated in a large interval, then, the individual may no longer belong to the original geometry classification class, or even become malformation, and resulting in individual worse. Therefore, the genes of individual geometry variables and the genes of individual non-geometry variables should perform in different ways. According to the geometry constraint knowledge, the first-order coding of the parent mutants can be extracted to obtain the upper limit value and lower limit value of the mesh interval matrix corresponding to the genes of the parent geometry. Limiting the genes of the parent geometry should be changed in the corresponding mesh interval, to ensure the geometry of offspring still satisfy the geometry classification constraint. Intelligent Genetic Optimization Strategy for Stick The intelligent optimization design process for excavator stick can be divided into structural scheme design stage, structural optimized pre-processing stage, numerical optimization stage and structural optimized post-processing stage. The main steps of the intelligent optimization design process are shown in Fig.2 and illustrated as follows.