Affiliations: Institute of Vibration Engineering, Northwestern Polytechnical University, Xi'an 710072, China
Note: [*] Present address: School of Engineering, Nottingham Trent University, Nottingham NG1 4BU, UK. Email: wen.yang@ntu.ac.uk
Abstract: In order to further improve the convergence performance of available genetic algorithms (GAs), a new operator, namely Immigration Operator (IO), was proposed in this paper. Using the IO, an improved genetic algorithm was developed. To verify the effectiveness of the IO on improving the evolutionary performances of the algorithm, two benchmarking problems had been adopted. The first one is the typical simulation problem for searching the maximum value of the advanced Goldstein & Price function in a prescribed region. The second is the well-known Traveling Salesman problem (TSP). Subsequently, the improved algorithm was applied to search the effective criteria for monitoring the working condition of engine valves. The object inspected in the experiments was the sixth exhaust valve of a 6135-typed diesel engine. Both the simulated and practical experiments suggest that, after adopting the IO, a higher rate of convergence is achieved by the improved algorithm. Particularly in solving the kind of TSP problems, the crossover operator is handicapped in avoiding the morbid solution (i.e. the same city is traveled for multiple times in a same tour). In contrast, the IO provides an additional motivity for driving the evolution.
Keywords: immigration operator, genetic algorithm, traveling salesman problem
