A Novel Improved Accelerated PSO for Test-Sheet Composition

Abstract

To overcome the shortcomings in traditional methods that dissatisfy the examination requirements in the test-sheet composition problem, a novel robust hybrid meta-heuristic optimization algorithm in terms of analytic hierarchy process (AHP) and improved accelerated particle swarm optimization (APSO) is proposed. The improvement includes the addition of differential evolution (DE) mutation operator during the process when the particles updating so as to speed up convergence. Firstly, according to the requirements of the test-sheet composition, a multi-constrained multi-objective model of test-sheet composition is created. Secondly, AHP is used to work out the weights of all the test objectives, and then the multi-objective model is converted to the single objective model by the linear weighted sum. Finally, this hybrid meta-heuristic method ( DEAPSO) is used to fulfill test-sheet composition. To prove the performance of DEAPSO, DEAPSO is compared with other population-based optimization methods. The experimental results show that the proposed method can greatly improve composition speed and success rate.