Bernabe Dorronsoro

Universidad de Cádiz | UCA · Department of Computer Engineering

In the age of transformative artificial intelligence (AI), which has the potential to revolutionize our lives, this book provides a comprehensive exploration of successful research and applications in AI and data analytics. Covering innovative approaches, advanced algorithms, and data analysis methodologies, this book addresses complex problems ac...

The Bin Packing problem (BPP) is a classic optimization problem that is known for its applicability and complexity, which belongs to a special class of problems called NP-hard, in which, given a set of items of variable size, we search to accommodate them inside fixed size containers, seeking to optimize the number of containers to be used, that is...

The rapid technological growth in the computer industry has brought a wide variety of computer architectures and available programs with it. This makes the important problem of optimizing software performance increasingly complex. The reason is that automatic processing is required in order to consider in the decisions both the characteristics of t...

With the generalisation of distributed computing paradigms to sustain the surging demands for massive processing and data-analytic capabilities, the protection of the intellectual property tied to the executed programs transferred onto these remote shared platforms becomes critical. A more and more popular solution to this problem consists in apply...

The Bin Packing Problem (BPP) is a classic optimization problem that is known for its applicability and complexity, which belongs to a particular class of problems called NP-hard, in which, given a set of items of variable size, we search to accommodate them inside fixed size containers, seeking to optimize the number of containers to be used, that...

This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse information from different datasets. We also propose a new dataset, called UNK22. It is built from three of the...

An efficient public transport system is essential for sustainable city development, as it directly affects people’s welfare. This article addresses the urban public transport timetabling problem with multi-objective evolutionary algorithms, considering multiple vehicle types and respecting the public transport restrictions of local authorities. The...

This article presents the application of a learning-based optimization method to solve the Bus Synchronization Problem, a relevant problem in public transportation systems. The problem consists in synchronizing the timetable of buses to optimize the transfer of passengers between bus lines. A new problem model is proposed, extending previous formul...

This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse information from different datasets. Therefore, R-NIDS targets the design of more robust models, that generalize...

In this paper, we address the exact solution of the vertex bisection problem (VBP). We propose two novel B&B algorithms to solve VBP, which include new upper and lower bound constructive heuristics, and an efficient strategy to explore the combinatorial search space. The computational results show that the proposed algorithms clearly outperforms th...

Communication networks and systems are continuously threatened by a great variety of cybersecurity attacks coming from new malware that targets old and new systems’ vulnerabilities. In this sense, Intrusion Detection Systems (IDSs) and, specifically, Network IDSs (NIDSs) are used to count on robust methods and techniques to detect and classify secu...

This article presents how Virtual Savant (VS) can be used to automatically learn, from an exact algorithm, how to solve the basic independent Next Release Problem in a quick and accurate way. This variant of the Next Release Problem (NRP) is in essence a 0/1 Knapsack Problem and VS is applied to solve the underlying optimization problem. VS is a ge...

The study of dynamic multi-objective optimization problems (DMOP) is an area that has recently been receiving increased attention from researchers. Within the literature, various alternatives have been proposed to solve DMOPs, among them are the dynamic multi-objective evolutionary algorithms (DMOEA), which use stochastic methods to obtain solution...

We propose a new accurate Micro Genetic Algorithm ( GA) for multi-objective optimization problems that we call Micro-FAME (or FAME). The distinctive feature of FAME with respect to the other existing multi-objective algorithms in the literature is its high elitism and fast convergence, produced by the application of the evolution directly on the...

Particle Swarm Optimization algorithms (or PSO) have been widely studied in the Literature. It is known that they provide highly competitive results. However, they suffer from fast convergence to local optima. There exist works proposing the swarm decentralization by including some specific topologies in order to deal with this problem. These appro...

Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. T...

In this chapter, an analytical parameter tuning for the Archive Multi-Objective Simulated Annealing (AMOSA) with a fuzzy logic controller is proposed. The analytical tuning is used to compute the initial and final temperature, as well as the maximum metropolis length. The fuzzy logic controller is used to adjust the metropolis length for each tempe...

Dynamic optimization problems have attracted the attention of researchers due to their wide variety of challenges and their suitability for real-world problems. The application of hyper-heuristics to solve optimization problems is another area that has gained interest recently. These algorithms can apply a search space exploration method at differe...

Several previous works have shown how using prior knowledge within machine learning models helps to overcome the curse of dimensionality issue in high dimensional settings. However, most of these works are based on simple linear models (or variations) or do make the assumption of knowing a pre-defined variable grouping structure in advance, somethi...

Plug-in hybrid (PH) buses offer range and operating flexibility of buses with conventional internal combustion engines with environmental. However, when they are frequently charged, they also enable societal benefits (emissions- and noise-related) associated with electric buses. Thanks to geofencing, pure electric drive of PH buses can be assigned...

This volume constitutes the refereed proceedings of the Third International Conference on Optimization and Learning, OLA 2020, held in Cádiz, Spain, in February 2020. The 23 full papers were carefully reviewed and selected from 55 submissions. The papers presented in the volume focus on the future challenges of optimization and learning methods,...

This work presents a novel parallel branch and bound algorithm to efficiently solve to optimality a set of instances of the multi-objective flexible job-shop scheduling problem for the first time, to the very best of our knowledge. It makes use of the well-known NSGA-II algorithm to initialize its upper bound. The algorithm is implemented for share...

Content Distribution Networks (CDN) are key for providing worldwide services and content to end-users. In this work, we propose three multiobjective evolutionary algorithms for solving the problem of designing and optimizing cloud-based CDNs. We consider the objectives of minimizing the total cost of the infrastructure (including virtual machines,...

We present in this work the first parallel implementation of Virtual Savant (VS), a novel optimization method that is able to quickly generate pseudo-optimal solutions to a given combinatorial problem, thanks to its parallel pattern recognition engine. The proposed parallel implementation does not require any information exchange between the thread...

Dynamic optimization multi-objective problems (DMOPs) are characterized by the environmental changes they experiment . For these problems, optimization algorithms have limited time to find accurate results before every change. A common scenario in optimization is the presence of a decision maker (DM), which establishes preferences on the problem be...

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic...

This article presents the application of Virtual Savant to solve resource allocation problems, a widely-studied area with several real-world applications. Virtual Savant is a novel soft computing method that uses machine learning techniques to compute solutions to a given optimization problem. Virtual Savant aims at learning how to solve a given pr...

Computer-aided design (CAD) is a technological revolution, very powerful and with large applicability to problem solving. It is essential in many different disciplines ranging from architecture to education, medicine, physics, or gaming. In this work, we propose a novel CAD tool, called CADDi, to assist in the design of electric diagrams in the edu...

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 work addresses the multi-objective resource provisioning problem for building cloud-based CDNs. The optimization objectives are the minimization of VM, network and storage cost, and the maximization of the QoS for the end-user. A brokering model is proposed such that a single cloud-based CDN is able to host multiple content providers applying...

Pareto fronts found by a parallel Branch and bound. The Pareto fronts are from Fattahi instances set. The objectives are the makespan, max workload, and total workload. The optimal Pareto fronts found are from SFJS1 to SFJS10 and MSFJ1 to MFJS2.

FAME implementation based on the jMetal 5 project.

MANETs are specially vulnerable against security attacks. For protecting them, security solutions are traditionally addressed by the so-called intrusion detection and response systems. Nevertheless, using reactive (response) solutions, once the attack is detected, rely in long attack mitigation times. In this work, we propose the use of relay node...

Today's IT systems and IT processes must be ready to handle change in an efficient and responsive manner to allow businesses to both evolve and adapt to a changing world. In this paper we describe an approach that consists of using simulation based multi-objective optimization to select optimal ITIL change management process strategies that help IT...

We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first multi-objective cooperative coevolutionary algorithm based on PSO in the literature. SMPSO adopts a strategy for limiting the velocity of the par...

This chapter analyzes the use of adaptive neighborhoods based on coalitions in evolutionary optimization frameworks. First, we introduce the concepts of evolutionary algorithms, population topologies and coalitions. We integrate all these topics to study how to avoid some of the drawbacks of previous evolutionary algorithms and to remove their typi...

Sensitivity analysis is a mathematical tool that distributesthe uncertainty of the output of a model among its different input vari-ables. We use in this work the Extended Fourier Amplitude SensitivityTest to carefully analyze the impact of 54 LLVM code optimization op-erators on the execution time of nine benchmark software programs. Ex-periments...

We present a novel method to automatically generate new parallel solvers for optimization problems, called the Virtual Savant. It applies machine learning to model a reference algorithm (which is treated as a black box) from its solutions to a given problem, and after, it is able to efficiently and accurately reproduce its solutions on new unseen p...

This article presents an evolutionary approach for virtual machine planning under the model of virtual brokering for IaaS services in cloud computing. The proposed evolutionary algorithm provides an accurate technique for managing the cloud user requests and planning the effective utilization of virtual machines owned by the virtual broker. The mai...

This chapter presents a new kind of cloud brokering model called virtual broker. The virtual broker owns and manages what we call a virtual cloud, composed by a set of reserved VMs from a number of public cloud providers. This new broker sublets its resources to its customers as on-demand VMs, at lower prices than those offered in the market. This...

Support vector machines are widely used for classification and regression tasks. However, sequential implementations for support vector machines are usually unable to deal with the increasing size of current real-world learning problems. In this context, Intel\circleR Xeon Phi\TM~\sloppy processors allow easily incorporating high performance compu...

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...

This article studies the application of multiobjective evolutionary algorithms for solving the energy-aware scheduling problem of workflows in a distributed system that is composed by a federation of datacenters. Nowadays, energy efficiency is a major concern when using large distributed computing systems, including novel grid and cloud computing f...

This article presents a multiobjective approach for scheduling large workflows in distributed datacenters. We consider a realistic scheduling scenario of distributed cluster systems composed of multi-core computers, and a multi-objective formulation of the scheduling problem to minimize makespan, energy consumption and deadline violations. The stud...

The editors would like to thank the authors and reviewers for all the hard work towards ensuring the high standards of the special issue.

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network lik...

This book constitutes the refereed proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015, held in Albacete, Spain, in November 2015. The 31 revised full papers presented were carefully selected from 175 submissions. The papers are organized in topical sections on Bayesian networks and uncertainty mo...