Parallel Evolutionary Algorithm in Scheduling Work Packages to Minimize Duration of Software Project Management

Software project management problem mainly includes resources allocation and work packages scheduling. This paper presents an approach to Search Based Software Project Management based on parallel implementation of evolutionary algorithm on GPU. We redesigned evolutionary algorithm to cater for the purpose of parallel programming. Our approach aims to parallelize the genetic operators including: crossover, mutation and evaluation in the evolution process to achieve faster execution. To evaluate our approach, we conducted a “proof of concept” empirical study, using data from three real-world software projects. Both sequential and parallel version of a conventional single objective evolutionary algorithm are implemented. The sequential version is based on common programming approach using C++, and the parallel version is based on GPGPU programming approach using CUDA. Results indicate that even a relatively cheap graphic card (GeForce GTX 970) can speed up the optimization process significantly. We believe that deploy parallel evolutionary algorithm based on GPU may fit many applications for other software project management problems, since software projects often have complex inter-related work packages and resources, and are typically characterized by large scale problems which optimization process ought to be accelerated by parallelism.