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Big Data Optimization is the term used to refer to optimiza-
tion problems which have to manage very large amounts of data. In this
paper, we focus on the parallelization of metaheuristics with the Apache
Spark cluster computing system for solving multi-objective Big Data Op-
timization problems. Our purpose is to study the in
uence of accessing
data stored in the Hadoop File System (HDFS) in each evaluation step
of a metaheuristic and to provide a software tool to solve these kinds
of problems. This tool combines the jMetal multi-objective optimiza-
tion framework with Apache Spark. We have carried out experiments
to measure the performance of the proposed parallel infrastructure in
an environment based on virtual machines in a local cluster comprising
up to 100 cores. We obtained interesting results for computational e ort
and propose guidelines to face multi-objective Big Data Optimization
problems.Ministerio de Educación y Ciencia TIN2014-58304-RJunta de Andalucía P11-TIC-7529Junta de Andalucía P12-TIC-151
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