Antonio Jesús Nebro

• PhD in Computer Science
• Professor (Associate) at University of Malaga

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algori...

The rapid evolution of mobile communications, remarkably the fifth generation (5G) and research-stage sixth (6G), highlights the need for numerous heterogeneous base stations to meet high demands. However, the deployment of many base stations entails a high energy cost, which contradicts the concept of green networks promoted by next-generation net...

Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform be...

NSGA-II is, by far, the most popular metaheuristic that has been adopted for solving multi-objective optimization problems. However, its most common usage, particularly when dealing with continuous problems, is circumscribed to a standard algorithmic configuration similar to the one described in its seminal paper. In this work, our aim is to show t...

Multi-objective particle swarm optimizers (MOPSOs) have been widely used to deal with optimization problems having two or more conflicting objectives. As happens with other metaheuristics, finding the most adequate parameters settings for MOPSOs is not a trivial task, and it is even harder to choose structural components that determine the algorith...

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 d...

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 consump...

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction a...

jMetal is a Java-based framework for multi-objective optimization with metaheuristics that is widely used in the field. The jMetal project started in 2006, and it is continuously evolving. In this chapter, we describe a set of new features that are in current development, aimed at facilitating the analysis of the results of executing multi-objectiv...

In the field of complex problem optimization with metaheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker’s preferences, or algorithms. However, there is a lack of approaches where ontologies are applied in a direct way into the optimization process, with the aim of enhan...

The computational reconstruction of Gene Regulatory Networks (GRNs) from gene expression data has been modelled as a complex optimisation problem, which enables the use of sophisticated search methods to address it. Among these techniques, particle swarm optimisation based algorithms stand out as prominent techniques with fast convergence and accur...

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algori...

Finding the orientation of a ligand (small molecule) with the lowest binding energy to the macromolecule (receptor) is a complex optimization problem, commonly called ligand–protein docking. This problem has been usually approached by minimizing a single objective that corresponds to the final free energy of binding. In this work, we propose a new...

Motivation: Multiple sequence alignment (MSA) consists of finding the optimal alignment of three or more biological sequences to identify highly conserved regions that may be the result of similarities and relationships between the sequences. MSA is an optimisation problem with NP-hard complexity, because the time needed to find optimal alignments...

Many Pareto-based multiobjective evolutionary algorithms require ranking the solutions of the population in each iteration according to the dominance principle, which can become a costly operation particularly in the case of dealing with many-objective optimization problems. In this article, we present a new efficient algorithm for computing the no...

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issu...

The efficient calibration of hydrologic models allows experts to evaluate past events in river basins, as well as to describe new scenarios and predict possible future floodings. A difficulty in this context is the need to adjust a large number of parameters in the model to reduce prediction errors. In this work, we address this issue with two comp...

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming l...

jMetal is a Java-based framework for multi-objective optimization with metaheuristics providing, among other features, a wide set of algorithms that are representative of the state-of-the-art. Although it has become a widely used tool in the area, it lacks support for automatic tuning of algorithm parameter settings, which can prevent obtaining acc...

Multi-objective optimization deals with problems having two or more conflicting objectives that have to be optimized simultaneously. When the objectives change somehow with time, the problems become dynamic, and if the decision maker indicates preferences at runtime, then the algorithms to solve them become interactive. In this paper, we propose th...

Industry 4.0 is revolutionizing decision making processes within the manufacturing industry. Among the technological portfolio enabling this revolution, the late literature has capitalized on the potential of data analytics for improving the production cycle at different stages, from resource provisioning to planning, delivery and storage. However,...

Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we prop...

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming l...

Molecular docking is a Bioinformatics method based on predicting the position and orientation of a small molecule or ligand when it is bound to a target macromolecule. This method can be modeled as an optimization problem where one or more objectives can be defined, typically around an energy scoring function. This paper reviews developments in the...

We propose a new method for multi-objective optimization, called Fuzzy Adaptive Multi-objective Evolutionary algorithm (FAME). It makes use of a smart operator controller that dynamically chooses the most promising variation operator to apply in the different stages of the search. This choice is guided by a fuzzy logic engine, according to the cont...

This paper focuses on modeling and solving a last-mile package delivery routing problem with third-party drop-off points. The study is applicable to small or medium-sized delivery companies, which use bikes for performing the routes in an influence area bounded to a city. This routing setup has been formulated as a multi-objective optimization prob...

Inference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the i...

Many Pareto-based multi-objective evolutionary algorithms require to rank the solutions of the population in each iteration according to the dominance principle, what can become a costly operation particularly in the case of dealing with many-objective optimization problems. In this paper, we present a new efficient algorithm for computing the non-...

The problem of molecular docking focuses on minimizing the binding energy of a complex composed by a ligand and a receptor. In this paper, we propose a new approach based on the joint optimization of three conflicting objectives: \(E_{inter}\) that relates to the ligand-receptor affinity, the \(E_{intra}\) characterizing the ligand deformity and th...

In the last years Data Science has emerged as one of the main technological enablers in many business sectors, including the manufacturing industry. Process engineers, who traditionally resorted to engineering tools for troubleshooting, have now embraced the support of data analysis to unveil complex patterns between process parameters and the qual...

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a u...

FAME implementation based on the jMetal 5 project.

Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically i...

Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. This paper proposes BIGOWL, an ontology to support knowledge management in Big D...

In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the pre...

This paper focuses on the design and implementation of a bike route optimization approach based on multi-objective bio-inspired heuristic solvers. The objective of this approach is to produce a set of Pareto-optimal bike routes that balance the trade-off between the length of the route and its safety level, the latter blending together the slope of...

Dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi-objective optimization wi...

Most of software solutions for phylogenetic inference try to find the best phylogenetic tree according to one reconstruction criterion, maximum parsimony or maximum likelihood, making the exploration of different hypothesis based on these two features a complex process. In this work, we present a novel software tool for phylogenetic inference based...

Descripción Qom es un modelo numérico hidrológico de abstracción de agua de lluvia del tipo concentrado, continuo y determinístico, que tiene como propósito separar los volúmenes de agua de lluvia caída en una cuenca hidrográfica en volúmenes de pérdidas y excesos. Objetivos La finalidad es la de contribuir a la toma de decisiones en proyectos que...

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 the solving of dynamic optimization problems from a number of external streaming data sources in Big Data contexts. In this paper, we describe the current status of the j...

Motivation: Multiple Sequence Alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponentially along with the number of sequences and their lengths. Additionally, to assess the quality of a MSA, a number of objectives can be taken into account, such...

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 th...

The alignment of more than two biological sequences is a widely used technique in a number of areas of computational biology. However, finding an optimal alignment has been shown to be an NP-complete optimization problem. Furthermore, Multiple Sequence Alignment (MSA) can be formulated according to more than one score function, leading to multi-obj...

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 metaheuristics with the Apache Spark cluster computing system for solving multi-objective Big Data Optimization problems. Our purpose is to study the influence of accessing data...

Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a three-objective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap...

Multiple sequence alignment (MSA) plays a core role in most bioinformatics studies and provides a framework for the analysis of evolution in biological systems. The MSA problem consists in finding an optimal alignment of three or more sequences of nucleotides or amino acids. Different scores have been defined to assess the quality of MSA solutions,...

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, w...

The human Epidermal Growth Factor (EGFR) plays an important role in signaling pathways, such as cell proliferation and migration. Mutations like G719S, L858R, T790M, G719S/T790M or T790M/L858R can alter its conformation, and, therefore, drug responses from lung cancer patients. In this context, candidate drugs are being tested and in silico studies...

Molecular docking is a complex optimization problem aimed at predicting the position of a ligand molecule in the active site of a receptor with the lowest binding energy. This problem can be formulated as a bi-objective optimization problem by minimizing the binding energy and the Root Mean Square Deviation (RMSD) difference in the coordinates of l...

Muchos de los algoritmos de optimización multi-objetivo más populares son poco eficaces al tratar con problemas de tres o más objetivos. Esto se debe en general al uso de estimadores de densidad, como la distancia de crowding de NSGA-II, que fueron diseñados cuando el principal reto era optimizar problemas de dos objetivos. En este artículo present...

Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel mu...

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a...

Phylogenetic inference is the process of searching and reconstructing the best phylogenetic tree that explains the evolution of species from a given data set. It is considered as an NP ‐hard problem due to the computational complexity required to find the optimal phylogenetic trees in the space of all the possible topologies. We have developed MO ‐...

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 improv...

The main objective of the molecular docking problem is to find a conformation between a small molecule (ligand) and a receptor molecule with minimum binding energy. The quality of the docking score depends on two factors: the scoring function and the search method being used to find the lowest binding energy solution. In this context, AutoDock 4.2...

Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimenta...

El Acoplamiento Molecular (Molecular Docking) es un problema de optimización de gran complejidad que consiste en predecir la orientación de dos moléculas: el ligando y el receptor, de manera que formen un complejo molecular energéticamente estable. El docking molecular es un problema tradicionalmente tratado con éxito mediante metaheurísticas para...

Abstract In this paper, we present a tool combining two software applications aimed at optimizing structural design problems of the civil engineering domain. Our approach lies in integrating an application for designing 2D and 3D bar structures, called Ebes, with the jMetal multi-objective optimization framework. The result is a software package th...

The design of bar structures in civil engineering is a complex problem when dealing with real-world structures. An approach to deal with these problems is to apply metaheuristics, which are stochastic methods based on iteratively producing and evaluating tentative solutions. In particular, we focus on multi-objective metaheuristics, as we consider...

In civil and industrial engineering, structural design optimization problems are usually characterized by the presence of multiple conflicting objectives, as to get the minimum investment cost and the maximum safety of the final design. This issue makes these problems to have not only one single solution, but a set them. Such solutions represent th...

Molecular docking is a method for structure-based drug design and structural molecular biology which attempts to predict the position and orientation of a small molecule (ligand) in relation to a protein (receptor) in order to produce a stable complex with a minimum binding energy. One of the most widely used software packages for this purpose is A...

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 th...

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 d...

This article introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary arch...

Algorithms based on the Particle Swarm Optimization (PSO) scheme have become popular to solve both single- and multi-objective optimization problems. In this paper, we focus on SMPSO, a PSO designed to cope with this second group of problems. Taking it as our starting point, we analyze different leader selection schemes, which give rise to four new...

Solution of Abstract Optimization problems with two or more conflicting functions or objectives by using metaheuristics has attracted attention of researches and become a rapidly developing area known as Multiobjective Optimization. Metaheuristics are non-exact techniques aimed to produce satisfactory solutions to complex optimization problems wher...

Solution of Abstract Optimization problems with two or more conflicting functions or objectives by using metaheuristics has attracted attention of researches and become a rapidly developing area known as Multiobjective Optimization. Metaheuristics are non-exact techniques aimed to produce satisfactory solutions to complex optimization problems wher...

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 a...

In recent years, the application of metaheuristic techniques to solve multi‐objective optimization problems has become an active research area. Solving this kind of problems involves obtaining a set of Pareto‐optimal solutions in such a way that the corresponding Pareto front fulfils the requirements of convergence to the true Pareto front and unif...

This paper describes jMetal, an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving multi-objective optimization problems. jMetal includes a number of classic and modern state-of-the-art optimizers, a wide set of benchmark problems, and a set of well-known quality indicators to ass...

Wireless sensor network layout, also known as sensor node deployment, is a complex NP-complete optimization task that determines most of the functioning features of a wireless sensor network. Coverage, connectivity and lifetime (handled through its opposing parameter, power consumption), are three of the most important characteristics of the servic...

This chapter introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary archi...

The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime. This problem has a capital importance for software companies. In the SPS problem, the total budget and human resources involved in software development must be optimally managed in order to end up with a successful project. Com...

One important issue addressed by software companies is to determine which features should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while entailing the minimum cost for the company. This problem is known as the Next Release Problem (NRP). Since minimizing the total c...

MOEA/D is a multi-objective optimization algorithm based on decomposition, which consists in dividing a multi-objective problem into a number of single-objective sub-problems. This work presents two variants, called pMOEA/Dv1 and pMOEA/Dv2, of a new parallel model of MOEA/D that have been developed under the observation that different sub-problems...

Solving optimization problems using a reduced number of objective function evaluations is an open issue in the design of multi-objective optimization metaheuristics. The usual approach to analyze the behavior of such techniques is to choose a benchmark of known problems, to perform a predetermined number of function evaluations, and then, apply a s...

To evaluate the search capabilities of a multiobjective algorithm, the usual approach is to choose a benchmark of known problems, to perform a fixed number of function evaluations, and to apply a set of quality indicators. However, while real problems could have hundreds or even thousands of decision variables, current benchmarks are normally adopt...

jMetal is a Java-based framework for multi-objective optimization using metaheuristics. It is a flexible, extensible, and easy-to-use software package that has been used in a wide range of applications. In this paper, we describe the design issues underlying jMetal, focusing mainly on its internal architecture, with the aim of offering a comprehens...

Automatic cell planning (ACP) is an optimization problem from the mobile telecommunications domain that addresses finding the location of the network antennae as well as their parameter settings in order to satisfy several cellular operator requirements. Due to its NP-hard complexity, evolutionary techniques have become popular for solving ACP inst...

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the frequency assignment problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real-world instances of FAP typically involve very large networ...

MOEA/D is a multi-objective metaheuristic which has shown a remarkable performance when solving hard optimization problems. In this paper, we propose a thread-based parallel version of MOEA/D designed to be executed on modern multi-core processors. Our interest is to study the potential benefits of the parallel approach in terms of speed-ups and th...

Since its appearance, Particle Swarm Optimization (PSO) has become a very popular technique for solving optimization problems because of both its simplicity and its fast convergence properties. In the last few years there has been a variety of proposals for extending it to handle with multiples objectives. Although many of them keep the same proper...

This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace existing individuals in the population after each iteration. The...

Planning a cellular phone network makes engineers to face a number of challenging optimization problems. This paper addresses the solution of one of these problems, Automatic Cell Planning (ACP), which lies in positioning the antennae of the network and configuring them properly in order to meet several objectives and constraints. This paper approa...

In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrained Multi-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high...

Genetic Algorithms (GAs) are among the most popular techniques to solve multi-objective optimization problems, with NSGA-II being the most well-known algorithm in the field. Although most of multi-objective GAs (MOGAs) use a generational scheme, in the last few years some proposals using a steady-state scheme have been developed. However, studies a...

This chapter aims to address some of the fundamental issues that are often encountered in optimization problems, making them difficult to solve. These issues include premature convergence, ruggedness, causality, deceptiveness, neutrality, epistasis, robustness, overfitting, oversimplification, multi-objectivity, dynamic fitness, the No Free Lunch T...

One of the first issues which has to be taken into account by software companies is to determine what should be included in the next release of their prod-ucts, in such a way that the highest possible number of customers get satisfied while this entails a mini-mum cost for the company. This problem is known as the Next Release Problem (NRP). Since...

This work presents the application of a parallel cooperative optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation implies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configuration...