Multi-objective metaheuristics have become popular techniques for dealing with complex
optimization problems composed of a number of conflicting functions. Nowadays, we are in …

Abstract Big Data Optimization is the term used to refer to optimization problems which have
to manage very large amounts of data. In this paper, we focus on the parallelization of …

In recent years, metaheuristics have been a convincing solution for solving many types of
complex optimization problems. Efficient execution for the most variants of metaheuristics eg …

This paper analyzes a multi-objective variant of the well-known Traveling Salesman
Problem (TSP) and the Traveling Repairman Problem (TRP) in order to address the …

jMetaISP is a framework for dynamic multi-objective Big Data optimization. It combines the
jMetal multi-objective framework with the Apache Spark cluster computing system to allow …

Over the past decade the rapid rise of creating data in all domains of knowledge such as
traffic, medicine, social network, industry, etc., has highlighted the need for enhancing the …

Recently, clustering techniques gained more importance due to huge range of applications
in the field of data mining, pattern recognition, data clustering, bio informatics and many …

Fusion effect of SVM in the Spark architecture for speech data mining in cluster structure is
studied in this manuscript. Based on the information entropy of nodes, the data in clusters …

We formulate and transform a real-world combina-torial problem into a constraint satisfaction
problem: choose a restricted set of containers from a warehouse, such that the elements …

We formulate and transform a real-world combinatorial problem into a constraint satisfaction
problem: choose a restricted set of containers from a warehouse, such that the elements …