Search Based Software Testing (SBST) is one of the most explored fields in Software Testing. It suffers from the optimization problem to execute the Software Under Test (SUT). This problem is addressed mostly using Ge...
详细信息
ISBN:
(纸本)9781479939152
Search Based Software Testing (SBST) is one of the most explored fields in Software Testing. It suffers from the optimization problem to execute the Software Under Test (SUT). This problem is addressed mostly using geneticalgorithm (GA) and it involves three operations namely selection, crossover and mutation to accomplish a global search to yield fitness solution to run the SUT successfully. In existing work, GA is combined with Hadoop MapReduce to give parallel genetic algorithm (PGA). Here, mapper function performs parallel fitness computation and reducer function performs the GA. This PGA generates test suite that makes the entire SUT to get executed. This paper makes an attempt to existing by parallelizing fitness calculation and GA operations to generate search test data for the SUT based on branch coverage criteria.
This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with consideration of practical generator constraints. The range of partial po...
详细信息
ISBN:
(纸本)9781424457939
This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with consideration of practical generator constraints. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel particle swarm optimization. To validate the robustness of the proposed IPPSO approach, it is tested to two test systems having nonconvex solution spaces, 26-bus (6 generating units) with consideration of valve point effects, network and with 15 generating units. The simulation results compared with recent global optimization methods (GA, MTS, SA, PSO and ICA-PSO). The outcome of the comparisons shows the effectiveness of the proposed IPPSO approach in terms of solution quality, consistency and reduced computational time compared to various methods available in the literature survey.
暂无评论