Experimental Study of Distributed Differential Evolution Based on Different Platforms

With the increasing complexity of real-world optimization problems, many challenges appear to evolutionary algorithms (EAs). When solving these time-consuming or high-complexity problems, although EAs can guarantee the high quality of solutions, the intolerable time costs will influence their availabilities drastically. Thus, many attempts have been made to overcome that problem. With the rapid development of the distributed computing paradigm and platforms, such as the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP), distributed computing has become readily available and affordable for realizing more powerful EAs. In order to find out whether these platforms have any particular difficulties or preference, whether one of them would be more suitable for EAs, we analyze the performance of different distributed EAs (DEAs) based on different distributed computing platforms, using differential evolution (DE) as an example. Finally, we find out that both MPI and OpenMP have their own superiorities and they can improve the speedup obviously. However, MPI is more suitable for computationally expensive problems and can achieve higher speedup than OpenMP.