jMetalSP: A framework for dynamic multi-objective big data optimization

Abstract Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.