Systolic genetic search, a systolic computing-based metaheuristic

In this paper,wepropose a newparallel optimization algorithm that combines ideas from the fields of metaheuristics and Systolic Computing. The algorithm, called Systolic Genetic Search (SGS), is designed to explicitly exploit the high degree of parallelism available in modern Graphics Processing Unit (GPU) architectures. In SGS, solutions circulate synchronously through a grid of processing cells, which apply adapted evolutionary operators on their inputs to compute their outputs that are then ejected from the cells and continue moving through the grid. Four different variants of SGS are experimentally studied for solving two classical benchmarking problems and a real-world application. An extensive experimental analysis, which considered several instances for each problem, shows that three of the SGS variants designed are highly effective since they can obtain the optimal solution in almost every execution for the instances and problems studied, as well as they outperform a Random Search (sanity check) and two Genetic Communicated by V. Loia. M. Pedemonte (B) Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, 11300 Montevideo, Uruguay e-mail: [email protected] F. Luna Depto. de Ingeniería de Sistemas Informáticos y Telemáticos, Centro Universitario de Mérida, Universidad de Extremadura, Santa Teresa de Jornet, 28, 06800 Mérida, Spain e-mail: [email protected] E. Alba Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain e-mail: [email protected] Algorithms. The parallel implementation on GPU of the proposed algorithm has achieved a high performance obtaining runtime reductions from the sequential implementation that, depending on the instance considered, can arrive to around a hundred times, and have also exhibited a good scalability behavior when solving highly dimensional problem instances.