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Dr. Eneko Osaba
Tecnalia Research & Innovation, 48160 Derio, Spain

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Research Keywords & Expertise

0 Artificial Intelligence
0 Combinatorial Optimization
0 Metaheuristics
0 Swarm Intelligence
0 Bioinspired optimization

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Metaheuristics
Combinatorial Optimization
Swarm Intelligence
Artificial Intelligence
Bioinspired optimization

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Journal article
Published: 20 August 2021 in Swarm and Evolutionary Computation
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Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.

ACS Style

Antonio LaTorre; Daniel Molina; Eneko Osaba; Javier Poyatos; Javier Del Ser; Francisco Herrera. A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation 2021, 67, 100973 .

AMA Style

Antonio LaTorre, Daniel Molina, Eneko Osaba, Javier Poyatos, Javier Del Ser, Francisco Herrera. A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation. 2021; 67 ():100973.

Chicago/Turabian Style

Antonio LaTorre; Daniel Molina; Eneko Osaba; Javier Poyatos; Javier Del Ser; Francisco Herrera. 2021. "A prescription of methodological guidelines for comparing bio-inspired optimization algorithms." Swarm and Evolutionary Computation 67, no. : 100973.

Journal article
Published: 30 July 2021 in Information
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The current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.

ACS Style

Juncal Alonso; Leire Orue-Echevarria; Eneko Osaba; Jesús López Lobo; Iñigo Martinez; Josu Diaz de Arcaya; Iñaki Etxaniz. Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum. Information 2021, 12, 308 .

AMA Style

Juncal Alonso, Leire Orue-Echevarria, Eneko Osaba, Jesús López Lobo, Iñigo Martinez, Josu Diaz de Arcaya, Iñaki Etxaniz. Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum. Information. 2021; 12 (8):308.

Chicago/Turabian Style

Juncal Alonso; Leire Orue-Echevarria; Eneko Osaba; Jesús López Lobo; Iñigo Martinez; Josu Diaz de Arcaya; Iñaki Etxaniz. 2021. "Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum." Information 12, no. 8: 308.

Review
Published: 18 May 2021 in Springer Tracts in Nature-Inspired Computing
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Optimization is one of the most studied fields within the wider area of artificial intelligence. In the current literature, hundreds of works can be found focused on solving many diverse problems of this kind by resorting to a vast spectrum of solvers. In this context, Swarm Intelligence methods have gained significant popularity in the related community, maintaining a constant momentum in recent years, and having been applied to problems coming from a wide variety of real-world contexts. This chapter contributes to this line by presenting a systematic overview of Swarm Intelligence solvers applied to different branches of optimization problems. To do that, we have focused our attention on four of the most intensively studied application fields: transportation, energy, medicine, and industry. Apart from this systematic review, we also share in this paper our envisioned status of this area, by identifying the most interesting opportunities. These open challenges should stimulate the scientific efforts made by the community in the upcoming years.

ACS Style

Eneko Osaba; Xin-She Yang. Applied Optimization and Swarm Intelligence: A Systematic Review and Prospect Opportunities. Springer Tracts in Nature-Inspired Computing 2021, 1 -23.

AMA Style

Eneko Osaba, Xin-She Yang. Applied Optimization and Swarm Intelligence: A Systematic Review and Prospect Opportunities. Springer Tracts in Nature-Inspired Computing. 2021; ():1-23.

Chicago/Turabian Style

Eneko Osaba; Xin-She Yang. 2021. "Applied Optimization and Swarm Intelligence: A Systematic Review and Prospect Opportunities." Springer Tracts in Nature-Inspired Computing , no. : 1-23.

Review
Published: 18 May 2021 in Springer Tracts in Nature-Inspired Computing
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Since the proposal of metaheuristics such as Genetic Algorithm, Particle Swarm Optimization or Ant Colony Optimization, methods inheriting their core concepts have gained great popularity, lasting this momentum until the present day. For the modeling of these algorithms, a myriad of inspirational sources have been deemed. Some examples of inspiration are the behavioral patterns of animals, genetic inheritance mechanisms, physical phenomena or social behavior of human beings. In this regard, the number of methods finding their inspiration in soccer concepts has grown considerably in the last years. We can find examples such as Soccer Game Optimization, World Cup Optimization or the Golden Ball, which have attained a consistent literature around them. This chapter will systematically review the state of the art around this specific kind of metaheuristics, highlighting their applications in the literature.

ACS Style

Eneko Osaba; Xin-She Yang. Soccer-Inspired Metaheuristics: Systematic Review of Recent Research and Applications. Springer Tracts in Nature-Inspired Computing 2021, 81 -102.

AMA Style

Eneko Osaba, Xin-She Yang. Soccer-Inspired Metaheuristics: Systematic Review of Recent Research and Applications. Springer Tracts in Nature-Inspired Computing. 2021; ():81-102.

Chicago/Turabian Style

Eneko Osaba; Xin-She Yang. 2021. "Soccer-Inspired Metaheuristics: Systematic Review of Recent Research and Applications." Springer Tracts in Nature-Inspired Computing , no. : 81-102.

Journal article
Published: 04 May 2021 in Information Sciences
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Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned evolutionary multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process.

ACS Style

Eneko Osaba; Javier Del Ser; Aritz D. Martinez; Jesus L. Lobo; Francisco Herrera. AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking. Information Sciences 2021, 570, 577 -598.

AMA Style

Eneko Osaba, Javier Del Ser, Aritz D. Martinez, Jesus L. Lobo, Francisco Herrera. AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking. Information Sciences. 2021; 570 ():577-598.

Chicago/Turabian Style

Eneko Osaba; Javier Del Ser; Aritz D. Martinez; Jesus L. Lobo; Francisco Herrera. 2021. "AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking." Information Sciences 570, no. : 577-598.

Journal article
Published: 28 April 2021 in Swarm and Evolutionary Computation
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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, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.

ACS Style

Eneko Osaba; Esther Villar-Rodriguez; Javier Del Ser; Antonio J. Nebro; Daniel Molina; Antonio LaTorre; Ponnuthurai N. Suganthan; Carlos A. Coello Coello; Francisco Herrera. A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems. Swarm and Evolutionary Computation 2021, 64, 100888 .

AMA Style

Eneko Osaba, Esther Villar-Rodriguez, Javier Del Ser, Antonio J. Nebro, Daniel Molina, Antonio LaTorre, Ponnuthurai N. Suganthan, Carlos A. Coello Coello, Francisco Herrera. A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems. Swarm and Evolutionary Computation. 2021; 64 ():100888.

Chicago/Turabian Style

Eneko Osaba; Esther Villar-Rodriguez; Javier Del Ser; Antonio J. Nebro; Daniel Molina; Antonio LaTorre; Ponnuthurai N. Suganthan; Carlos A. Coello Coello; Francisco Herrera. 2021. "A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems." Swarm and Evolutionary Computation 64, no. : 100888.

Conference paper
Published: 27 October 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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The term Swarm Robotics collectively refers to a population of robotic devices that efficiently undertakes diverse tasks in a collaborative way by virtue of computational intelligence techniques. This paradigm has given rise to a profitable stream of contributions in recent years, all sharing a clear consensus on the performance benefits derived from the increased exploration capabilities offered by Swarm Robotics. This manuscript falls within this topic: specifically, it gravitates on an heterogeneous Swarm Robotics system that relies on Stochastic Diffusion Search (SDS) as the coordination heuristics for the exploration, location and delimitation of areas scattered over the area in which robots are deployed. The swarm is composed by agents of diverse kind, which can be ground robots or flying devices. These agents communicate to each other and cooperate towards the accomplishment of the exploration tasks comprising the mission of the overall swarm. Furthermore, maps contain several obstacles and dangers, implying that in order to enter a specific area, robots should meet certain conditions. Experiments are conducted over three different maps and three implemented solving approaches. Conclusions are drawn from the obtained results, confirming that i) SDS allows for a lightweight, heuristic mechanism for the coordination of the robots; and ii) the most efficient swarming approach is the one comprising a heterogeneity of ground and aerial robots.

ACS Style

Eneko Osaba; Javier Del Ser; Xabier Jubeto; Andrés Iglesias; Iztok Fister; Akemi Gálvez. Distributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Search. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 79 -91.

AMA Style

Eneko Osaba, Javier Del Ser, Xabier Jubeto, Andrés Iglesias, Iztok Fister, Akemi Gálvez. Distributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Search. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():79-91.

Chicago/Turabian Style

Eneko Osaba; Javier Del Ser; Xabier Jubeto; Andrés Iglesias; Iztok Fister; Akemi Gálvez. 2020. "Distributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Search." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 79-91.

Journal article
Published: 26 October 2020 in IEEE Intelligent Transportation Systems Magazine
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The articles in this special section focus on data driven optimization for transportation and smart mobility applications. We live in an era of major societal and technological changes. Transportation de-carbonization and postindustrial demographic trends, such as massive migrations and an aging society, generate new challenges for cities, making the efficient and sustainable management of services and resources more necessary than ever. Cities must evolve, transform, and become smart to cope with these realities. According to the literature, a city can be referred to as smart when investments in human and social capital and traditional (transportation) and modern [information and communications technology (ICT)] communication infrastructure fuel sustainable economic growth and high quality of life, with a wise management of natural resources, through participatory government.

ACS Style

Eneko Osaba; Javier J. Sanchez Medina; Eleni I. Vlahogianni; Xin-She Yang; Antonio D. Masegosa; Joshue Perez Rastelli; Javier Del Ser. Data-Driven Optimization for Transportation Logistics and Smart Mobility Applications [Guest Editorial]. IEEE Intelligent Transportation Systems Magazine 2020, 12, 6 -9.

AMA Style

Eneko Osaba, Javier J. Sanchez Medina, Eleni I. Vlahogianni, Xin-She Yang, Antonio D. Masegosa, Joshue Perez Rastelli, Javier Del Ser. Data-Driven Optimization for Transportation Logistics and Smart Mobility Applications [Guest Editorial]. IEEE Intelligent Transportation Systems Magazine. 2020; 12 (4):6-9.

Chicago/Turabian Style

Eneko Osaba; Javier J. Sanchez Medina; Eleni I. Vlahogianni; Xin-She Yang; Antonio D. Masegosa; Joshue Perez Rastelli; Javier Del Ser. 2020. "Data-Driven Optimization for Transportation Logistics and Smart Mobility Applications [Guest Editorial]." IEEE Intelligent Transportation Systems Magazine 12, no. 4: 6-9.

Journal article
Published: 23 October 2020 in Information Fusion
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Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyperparametric configurations with improved performance for a given task, to the optimization of the model’s parameters as a replacement for gradient-based solvers. Indeed, the literature is rich in proposals showcasing the application of assorted nature-inspired approaches for these tasks. In this work we comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: (a) optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in Deep Learning, and a taxonomy associated with an in-depth analysis of the literature, (b) critical methodological analysis (How?), which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and (c) challenges and new directions of research (What can be done, and what for?). In summary, three axes – optimization and taxonomy, critical analysis, and challenges – which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.

ACS Style

Aritz D. Martinez; Javier Del Ser; Esther Villar-Rodriguez; Eneko Osaba; Javier Poyatos; Siham Tabik; Daniel Molina; Francisco Herrera. Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges. Information Fusion 2020, 67, 161 -194.

AMA Style

Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera. Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges. Information Fusion. 2020; 67 ():161-194.

Chicago/Turabian Style

Aritz D. Martinez; Javier Del Ser; Esther Villar-Rodriguez; Eneko Osaba; Javier Poyatos; Siham Tabik; Daniel Molina; Francisco Herrera. 2020. "Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges." Information Fusion 67, no. : 161-194.

Conference paper
Published: 15 June 2020 in Lecture Notes in Computer Science
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Functional networks are a powerful extension of neural networks where the scalar weights are replaced by neural functions. This paper concerns the problem of parametric learning of the associative model, a functional network that represents the associativity operator. This problem can be formulated as a nonlinear continuous least-squares minimization problem, solved by applying a swarm intelligence approach based on a modified memetic self-adaptive version of the firefly algorithm. The performance of our approach is discussed through an illustrative example. It shows that our method can be successfully applied to solve the parametric learning of functional networks with unknown functions.

ACS Style

Akemi Gálvez; Andrés Iglesias; Eneko Osaba; Javier Del Ser. Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm. Lecture Notes in Computer Science 2020, 566 -579.

AMA Style

Akemi Gálvez, Andrés Iglesias, Eneko Osaba, Javier Del Ser. Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm. Lecture Notes in Computer Science. 2020; ():566-579.

Chicago/Turabian Style

Akemi Gálvez; Andrés Iglesias; Eneko Osaba; Javier Del Ser. 2020. "Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm." Lecture Notes in Computer Science , no. : 566-579.

Conference paper
Published: 15 June 2020 in Parallel Problem Solving from Nature – PPSN XV
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Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.

ACS Style

Eneko Osaba; Javier Del Ser; Xin-She Yang; Andres Iglesias; Akemi Galvez. COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking. Parallel Problem Solving from Nature – PPSN XV 2020, 244 -256.

AMA Style

Eneko Osaba, Javier Del Ser, Xin-She Yang, Andres Iglesias, Akemi Galvez. COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking. Parallel Problem Solving from Nature – PPSN XV. 2020; ():244-256.

Chicago/Turabian Style

Eneko Osaba; Javier Del Ser; Xin-She Yang; Andres Iglesias; Akemi Galvez. 2020. "COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking." Parallel Problem Solving from Nature – PPSN XV , no. : 244-256.

Chapter
Published: 20 February 2020 in Nature-Inspired Computation in Navigation and Routing Problems
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Route planning is a research topic in artificial intelligence of paramount importance for a plethora of applications related to transportation and mobility. Problems related to the discovery of optimal routes have been at the core of hundreds of research studies reported to date in different scientific venues and tier-one journals. In this context, the vehicle routing problem (VRP) is arguably one of the most studied problems in this knowledge stream. Since its inception more than fifty years ago, manifold studies have been developed over decades of intense research efforts, proposing new realistic problem formulations and sophisticated algorithmic methods for its efficient solving. Such a vibrant activity has been propelled by the ever-growing needs and resource availability of the time being, which have spanned new challenging conditions for practical VRP problems. This is the main reason why this field of study, today, grasps more interest from the research community than ever. In this overview, we profoundly review the recent history and current status of the VRP and related variants. First, the important characteristics and variants of this problem are described in detail, followed by a systematic review of recent advances reported in the literature. We place special emphasis on bioinspired optimization algorithms published in the last years. On a closing note, the manuscript rounds up its literature overview with the envisioned status of this research area, exposed in the form of open challenges that still remain insufficiently addressed, and that should thus guide research efforts in the future.

ACS Style

Eneko Osaba; Xin-She Yang; Javier Del Ser. Is the Vehicle Routing Problem Dead? An Overview Through Bioinspired Perspective and a Prospect of Opportunities. Nature-Inspired Computation in Navigation and Routing Problems 2020, 57 -84.

AMA Style

Eneko Osaba, Xin-She Yang, Javier Del Ser. Is the Vehicle Routing Problem Dead? An Overview Through Bioinspired Perspective and a Prospect of Opportunities. Nature-Inspired Computation in Navigation and Routing Problems. 2020; ():57-84.

Chicago/Turabian Style

Eneko Osaba; Xin-She Yang; Javier Del Ser. 2020. "Is the Vehicle Routing Problem Dead? An Overview Through Bioinspired Perspective and a Prospect of Opportunities." Nature-Inspired Computation in Navigation and Routing Problems , no. : 57-84.

Special issue paper
Published: 28 January 2020 in Expert Systems
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This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark.

ACS Style

Ana‐Maria Nogareda; Javier Del Ser; Eneko Osaba; David Camacho. On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems. Expert Systems 2020, 37, 1 .

AMA Style

Ana‐Maria Nogareda, Javier Del Ser, Eneko Osaba, David Camacho. On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems. Expert Systems. 2020; 37 (6):1.

Chicago/Turabian Style

Ana‐Maria Nogareda; Javier Del Ser; Eneko Osaba; David Camacho. 2020. "On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems." Expert Systems 37, no. 6: 1.

Data article
Published: 28 January 2020 in Data in Brief
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In this paper, the benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths is presented (AC-VRP-SPDVCFP). This problem is a specific multi-attribute variant of the well-known Vehicle Routing Problem, and it has been originally built for modelling and solving a real-world newspaper distribution problem with recycling policies. The whole benchmark is composed by 15 instances comprised by 50–100 nodes. For the design of this dataset, real geographical positions have been used, located in the province of Bizkaia, Spain. A deep description of the benchmark is provided in this paper, aiming at extending the details and experimentation given in the paper A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy (Osaba et al.) [1]. The dataset is publicly available for its use and modification.

ACS Style

Eneko Osaba. Benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths. Data in Brief 2020, 29, 105142 .

AMA Style

Eneko Osaba. Benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths. Data in Brief. 2020; 29 ():105142.

Chicago/Turabian Style

Eneko Osaba. 2020. "Benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths." Data in Brief 29, no. : 105142.

Journal article
Published: 13 December 2019 in Applied Soft Computing
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Detecting groups within a set of interconnected nodes is a widely addressed problem that can model a diversity of applications. Unfortunately, detecting the optimal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to providing an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti-Fortunato-Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform competitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come.

ACS Style

Eneko Osaba; Javier Del Ser; David Camacho; Miren Nekane Bilbao; Xin-She Yang. Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics. Applied Soft Computing 2019, 87, 106010 .

AMA Style

Eneko Osaba, Javier Del Ser, David Camacho, Miren Nekane Bilbao, Xin-She Yang. Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics. Applied Soft Computing. 2019; 87 ():106010.

Chicago/Turabian Style

Eneko Osaba; Javier Del Ser; David Camacho; Miren Nekane Bilbao; Xin-She Yang. 2019. "Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics." Applied Soft Computing 87, no. : 106010.

Book chapter
Published: 04 December 2019 in Swarm Intelligence - Recent Advances, New Perspectives and Applications
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Open access peer-reviewed chapter

ACS Style

Eneko Osaba; Esther Villar; Javier Del Ser. Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications. Swarm Intelligence - Recent Advances, New Perspectives and Applications 2019, 1 .

AMA Style

Eneko Osaba, Esther Villar, Javier Del Ser. Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications. Swarm Intelligence - Recent Advances, New Perspectives and Applications. 2019; ():1.

Chicago/Turabian Style

Eneko Osaba; Esther Villar; Javier Del Ser. 2019. "Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications." Swarm Intelligence - Recent Advances, New Perspectives and Applications , no. : 1.

Editorial
Published: 14 November 2019 in Journal of Computational Science
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Robotics have experienced a meteoric growth over the last decades, reaching unprecedented levels of distributed intelligence and self-autonomy. Today, a myriad of real-world scenarios can benefit from the application of robots, such as structural health monitoring, complex manufacturing, efficient logistics or disaster management. Related to this topic, there is a paradigm connected to Swarm Intelligence which is grasping significant interest from the Computational Intelligence community. This branch of knowledge is known as Swarm Robotics, which refers to the development of tools and techniques to ease the coordination of multiple small-sized robots towards the accomplishment of difficult tasks or missions in a collaborative fashion. The success of Swarm Robotics applications comes from the efficient use of smart sensing, communication and organization functionalities endowed to these small robots, which allow for collaborative information sensing, operation and knowledge inference from the environment. The numerous industrial and social applications that can be addressed efficiently by virtue of swarm robotics unleashes a vibrant research area focused on distributing intelligence among autonomous agents with simple behavioral rules and communication schedules, yet potentially capable of realizing the most complex tasks. In this context, we present and overview recent contributions reported around this paradigm, which serves as an exemplary excerpt of the potential of Swarm Robotics to become a major research catalyst of the Computational Intelligence arena in years to come.

ACS Style

Eneko Osaba; Javier Del Ser; Andres Iglesias; Xin-She Yang. Soft Computing for Swarm Robotics: New Trends and Applications. Journal of Computational Science 2019, 39, 101049 .

AMA Style

Eneko Osaba, Javier Del Ser, Andres Iglesias, Xin-She Yang. Soft Computing for Swarm Robotics: New Trends and Applications. Journal of Computational Science. 2019; 39 ():101049.

Chicago/Turabian Style

Eneko Osaba; Javier Del Ser; Andres Iglesias; Xin-She Yang. 2019. "Soft Computing for Swarm Robotics: New Trends and Applications." Journal of Computational Science 39, no. : 101049.

Journal article
Published: 01 August 2019 in Swarm and Evolutionary Computation
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ACS Style

Javier Del Ser; Eneko Osaba; Daniel Molina; Xin-She Yang; Sancho Salcedo-Sanz; David Camacho; Swagatam Das; Ponnuthurai N. Suganthan; Carlos Coello Coello; Francisco Herrera. Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation 2019, 48, 220 -250.

AMA Style

Javier Del Ser, Eneko Osaba, Daniel Molina, Xin-She Yang, Sancho Salcedo-Sanz, David Camacho, Swagatam Das, Ponnuthurai N. Suganthan, Carlos Coello Coello, Francisco Herrera. Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation. 2019; 48 ():220-250.

Chicago/Turabian Style

Javier Del Ser; Eneko Osaba; Daniel Molina; Xin-She Yang; Sancho Salcedo-Sanz; David Camacho; Swagatam Das; Ponnuthurai N. Suganthan; Carlos Coello Coello; Francisco Herrera. 2019. "Bio-inspired computation: Where we stand and what's next." Swarm and Evolutionary Computation 48, no. : 220-250.

Conference paper
Published: 19 July 2019 in Algorithms and Data Structures
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Border reconstruction is a key technology in medical image processing, where it is applied to identify and separate different tissues, organs, and tumors in diagnostic procedures. The classical approaches for this problem are based on either linear or polynomial functions to describe the border of the region of interest. However, little effort has been devoted to the more powerful case of rational functions, which extend the polynomial case by including extra degrees of freedom (the weights). As a consequence, rational functions are more difficult to compute. In this paper, we solve the problem by applying a nature-inspired swarm intelligence method called cuckoo search algorithm. The method is applied to two illustrative examples of medical images with satisfactory results.

ACS Style

Akemi Gálvez; Iztok Fister; Eneko Osaba; Javier Del Ser; Andrés Iglesias. Cuckoo Search Algorithm for Border Reconstruction of Medical Images with Rational Curves. Algorithms and Data Structures 2019, 320 -330.

AMA Style

Akemi Gálvez, Iztok Fister, Eneko Osaba, Javier Del Ser, Andrés Iglesias. Cuckoo Search Algorithm for Border Reconstruction of Medical Images with Rational Curves. Algorithms and Data Structures. 2019; ():320-330.

Chicago/Turabian Style

Akemi Gálvez; Iztok Fister; Eneko Osaba; Javier Del Ser; Andrés Iglesias. 2019. "Cuckoo Search Algorithm for Border Reconstruction of Medical Images with Rational Curves." Algorithms and Data Structures , no. : 320-330.

Conference paper
Published: 13 July 2019 in Proceedings of the Genetic and Evolutionary Computation Conference Companion
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ACS Style

Antonio D. Masegosa; Eneko Osaba; Juan S. Angarita-Zapata; Ibai Laña; Javier Del Ser. Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks. Proceedings of the Genetic and Evolutionary Computation Conference Companion 2019, 1312 -1320.

AMA Style

Antonio D. Masegosa, Eneko Osaba, Juan S. Angarita-Zapata, Ibai Laña, Javier Del Ser. Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2019; ():1312-1320.

Chicago/Turabian Style

Antonio D. Masegosa; Eneko Osaba; Juan S. Angarita-Zapata; Ibai Laña; Javier Del Ser. 2019. "Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks." Proceedings of the Genetic and Evolutionary Computation Conference Companion , no. : 1312-1320.