科研之友
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摘要
Automated generation of all feasible assembly sequences for a given product is highly desirable in manufacturing industry. Many researches in the past decades described efforts to find more efficient algorithms for assembly sequence planning. By combining bacterial chemotaxis (BC) with genetic algorithm (GA), a novel BC-GA-based hybrid algorithm (BGHA) for assembly sequence planning is proposed in this paper. Each assembly sequence is encoded into a chromosome, which can be manipulated by genetic operators. Each gene in chromosome is treated as a bacterium, which affects properties of genetic operators by various moving behavior. By injecting BC into the properties of genetic operators, it can keep diversity of the populations during evolution process. The proposed algorithm is tested and compared with GA and Fuzzy logic-GA. Results show that BGHA can upgrade the quality in solution searching and decrease the probability of trapping into local optimal solutions.
