Redesigning the jMetal Multi-Objective Optimization Framework

jMetal, an open source, Java-based framework for multi-objective optimization with metaheuristics, has become a valuable tool for many researches in the area as well as for some industrial partners in the last ten years. Our experience using and maintaining it during that time, as well as the received comments and suggestions, have helped us improve the jMetal design and identify significant features to incorporate. This paper revisits the jMetal architecture, describing its refined new design, which relies on design patterns, principles from object-oriented design, and a better use of the Java language features to improve the quality of the code, without disregarding jMetal ever goals of simplicity, facility of use, flexibility, extensibility and portability. Among the newly incorporated features, jMetal supports live interaction with running algorithms and parallel execution of algorithms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives

In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.Javier Del Ser acknowledges funding support from the Basque Government (consolidated research group MATHMODE, Ref. IT1294-19). Antonio J. Nebro is supported by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE) and the Andalusian PAIDI program with Grant P18-RT-2799

A Study of the Combination of Variation Operators in the NSGA-II Algorithm

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application rate along the search process allows to improve the static classical behavior. This way, we explore the combined use of three different operators (simulated binary crossover, differential evolution’s operator, and polynomial mutation) in the NSGA-II algorithm. We have considered two strategies for selecting the operators: random and adaptive. The resulting variants have been tested on a set of 19 complex problems, and our results indicate that both schemes significantly improve the performance of the original NSGA-II algorithm, achieving the random and adaptive variants the best overall results in the bi- and three-objective considered problems, respectively.UNIVERSIDAD DE MÁLAGA. CAMPUS DE EXCELENCIA INTERNACIONAL ANDALUCÍA TEC

Multi-objective metaheuristics for preprocessing EEG data in brain–computer interfaces

In the field of brain–computer interfaces, one of the main issues is to classify the electroencephalogram (EEG) accurately. EEG signals have a good temporal resolution, but a low spatial one. In this article, metaheuristics are used to compute spatial filters to improve the spatial resolution. Additionally, from a physiological point of view, not all frequency bands are equally relevant. Both spatial filters and relevant frequency bands are user-dependent. In this article a multi-objective formulation for spatial filter optimization and frequency-band selection is proposed. Several multi-objective metaheuristics have been tested for this purpose. The experimental results show, in general, that multi-objective algorithms are able to select a subset of the available frequency bands, while maintaining or improving the accuracy obtained with the whole set. Also, among the different metaheuristics tested, GDE3, which is based on differential evolution, is the most useful algorithm in this contextThis work has been funded by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR project).Publicad

Optimización de problemas multiobjetivo de Ingeniería Civil con jMetal

Este artículo describe el uso del framework de optimización multiobjetivo jMetal para afrontar la resolución de problemas de ingeniería civil; en particular, lo que se ha hecho ha sido integrar un software Open Source para el diseño de estructuras, denominado Ebes, con jMetal. De esta forma los ingenieros civiles tienen a su disposición una herramienta que les permite diseñar estructuras que luego pueden ser optimizadas con metaheurísticas multiobjetivo atendiendo a varios criterios, como minimizar el peso y minimizar la deformación. Por otro lado, este tipo de problemas pueden ser objeto de estudios por parte de investigadores del área de las metaheurísticas, que pueden usarlos como casos de estudio. Tras presentar tanto jMetal como Ebes, se detalla la integración de ambas herramientas, se presentan tres casos de estudio y se proponen algunas líneas abiertas de investigación.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Solving a Real-World Structural Optimization Problem With a Distributed SMS-EMOA Algorithm

This paper addresses a real-world optimization problem in civil engineering. It lies in the dimensioning of a 162m long bridge composed of 1584 bars so that both its weight and its deformation are to be minimized. Evaluating each possible configuration of the bridge takes several seconds and, as a consequence, running a metaheuristic for several thousands of evaluations would require many days on one single processor. Our approach has been to develop a distributed master/worker version of SMS-EMOA, an indicator-based multiobjective algorithm. By combining the Java implementation of the algorithm in jMetal with the Condor distributed scheduler, we have been able to use more than 350 cores to obtain accurate results in a reasonable amount of time.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

On the performance of SQL scalable systems on Kubernetes: a comparative study

The popularization of Hadoop as the the-facto standard platform for data analytics in the context of Big Data applications has led to the upsurge of SQL-on-Hadoop systems, which provide scalable query execution engines allowing the use of SQL queries on data stored in HDFS. In this context, Kubernetes appears as the leading choice to simplify the deployment and scaling of containerized applications; however, there is a lack of studies about the performance of SQL-on-Hadoop systems deployed on Kubernetes, and this is the gap we intend to fill in this paper. We present an experimental study involving four representative SQL scalable platforms: Apache Drill, Apache Hive, Apache Spark SQL and Trino. Concretely, we analyze the performance of these systems when they are deployed on a Hadoop cluster with Kubernetes by using the TPC-H benchmark. The results of our study can help practitioners and users about what they can expect in terms of performance if they plan to use the advantages of Kubernetes to deploy applications using the analyzed SQL scalable platforms.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: Universidad de Málaga / CBUA. This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE), Andalusian PAIDI program with grant P18-RT-2799, and by project ”Evolución y desarrollo de la plataforma DOP de Big Data” (702C2000044) under Andalusian “Programa de Apoyo a la I+D+i Empresarial”

Metaheuristic approaches for optimal broadcasting design in metropolitan MANETs

11th International Conference on Computer Aided Systems Theory. Las Palmas de Gran Canaria, Spain, February 12-16, 2007Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the duration of the operation itself. This research, which has been developed within the OPLINK project (http://oplink.lcc.uma.es), faces a wide study about this problem in metropolitan MANETs with up to seven different advanced multiobjective metaheuristics. They all compute Pareto fronts of solutions which empower a human designer with the ability of choosing the preferred configuration for the network. The quality of these fronts is evaluated by using the hypervolume metric. The obtained results show that the SPEA2 algorithm is the most accurate metaheuristic for solving the broadcasting problem.Publicad

Un Framework para Big Data Optimization Basado en jMetal y Spark

Las metaheurísticas multi-objetivo se han convertido en técnicas muy utilizadas para la resolución de problemas complejos de optimización compuestos de varias funciones objetivo en conflicto entre sí. Nos encontramos en la actualidad inmersos en la era del Big Data, por lo que los problemas multi-objetivo que surjan en este contexto cumplirán algunas de las cinco V’s que caracterizan a las aplicaciones Big Data (volumen, velocidad, variedad, veracidad, valor). Como consecuencia, las metaheurísticas deberán ser capaces de resolver problemas dinámicos, que pueden cambiar en el tiempo debido al procesamiento y análisis de diferentes fuentes de datos, que típicamente serán en streaming. En este trabajo presentamos el software jMetalSP, que combina el framework jMetal con Apache Spark. De esta forma, las metaheurísticas disponibles en jMetal se pueden adaptar fácilmente para resolver problemas dinámicos que se alimenten de distintas fuentes de datos en streaming, y que son gestionadas por Spark. Se describe la arquitectura de jMetalSP y se valida mediante un caso de uso realista basado en TSP bi-objetivo con datos abiertos reales de tráfico de la ciudad de Nueva York.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech