A high-performance approach on mechanism isomorphism identification based on an adaptive hybrid genetic algorithm for digital intelligent manufacturing

摘要

The graph theory is an important method to achieve conceptual design for mechanism. During the process of kinematic structures enumeration using graph theory, isomorphism identification of graphs is an NP complete problem. It is important to improve the isomorphism identification efficiency and reliability. To solve the problem, an adaptive hybrid genetic algorithm is presented by mixing the improved genetic algorithm and local search algorithm. The crossover rate and mutation rate can be designed as adaptive parameters. Hence, the crossover rate and mutation rate can sustain the variety of the population and adjust the evolution. In the meantime, the pseudo-crossover operator is introduced to improve the search efficiency. In the last, some examples are illustrated to show the high efficiency of the algorithm by comparing with the results in other literatures.