Fitness distance analysis for parallel genetic algorithm in the test task scheduling problem
The test task scheduling problem (TTSP) has attracted increasing attention due to the wide range of automatic test systems applications, despite the fact that it is an NP-complete problem. The main feature of TTSP is the close interactions between task sequence and the scheme choice. Based on this point, the parallel implantation of genetic algorithm, called Parallel Genetic Algorithm (PGA), is proposed to determine the optimal solutions. Two branchesthe tasks sequence and scheme choice run the classic genetic algorithm independently and they balance each other due to their interaction in the given problem. To match the frame of the PGA, a vector group encoding method is provided. In addition, the fitness distance coefficient (FDC) is first applied as the measurable step of landscape to analyze TTSP and guide the design of PGA when solving the TTSP. The FDC is the director of the search space of the TTSP, and the search space determinates the performance of PGA. The FDC analysis shows that the TTSP owes a large number of local optima. Strong space search ability is needed to solve TTSP better. To make PGA more suitable to solve TTSP, three crossover and four selection operations are adopted to find the best combination. The experiments show that due to the characteristic of TTSP and the randomness of the algorithm, the PGA has a low probability for optimizing the TTSP, but PGA with Nabel crossover and stochastic tournament selection performs best. The assumptions of FDC are consistent with the success rate of PGA when solving the TTSP.
parallel genetic algorithm test task scheduling problem fitness distance coefficient genetic operators