Parallel test task scheduling is always complex and difficult to optimize. Aiming at this problem, an improved Genetic Simulated Annealing Algorithm based on Petri net is posed to. At first, a Petri net model is established for the system, then the transition sequence is used as task scheduling sequence set path. Genetic Algorithm is introduced in order to get the optimal path. In the process of search, the sequence will be able to stimulate changes as chromosomes, selection, crossover and mutation. In order to prevent premature convergence of the algorithm appears, into the phenomenon of local optimal solution, the individual needs simulated annealing operation, and finally, we can get the shortest time to complete the test task scheduling sequence.
Modern gasoline engine spark advanced angle calibration is a multi-objective optimization problem, commonly used genetic algorithm to solve this problem. However, the traditional genetic algorithm tends to local optimum probability of a larger, easy to fall into premature, this defect is likely to cause the solution is not the optimal solution set. To address this issue, the non-dominated sorting genetic algorithm II for the spark advanced angle optimization, through crowding distance maintain the diversity, overcome super individuals overgrowth, improved genetic algorithm post search results. Experimental results show the effectiveness of this method.
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