Influence of parallel metrics in the analysis of parallel metaheuristic algorithms

High computational requirements of current problems have driven most researches towards efficient processing formulations which require the use of multiple processors interconnected, this is the foundation of the parallel processing mechanism. Among the metrics to measure the performance of parallel algorithms, the most important and used is the speedup, but in the scientific community does not exist a consent on its definition and use. The aim of this work is to study different alternatives evaluating parallel metaheuristics. This report presents the results of several experimental tests to show the use of the speedup evaluating the same parallel distributed Genetic Algorithm in different ways, to solve MAXSAT problem. Our experiments show that depending on how the algorithm speedup is evaluated, different results can be obtained. Taking into account the test results we can conclude that the best scenario for evaluating parallel algorithms is comparing algorithms with the same accuracy, defining the quality of the solutions as stop condition, because all executions reach the optimal value allowing fair comparisons.