Browsing by Author "Nebro, Antonio J."

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    Analyze, Sense, Preprocess, Predict, Implement, and Deploy (ASPPID): An incremental methodology based on data analytics for cost-efficiently monitoring the industry 4.0
    (2019-06) Para, Jesus; Del Ser, Javier; Nebro, Antonio J.; Zurutuza, Urko; Herrera, Francisco; IA
    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, such a promising role of data analytics has been so far explored without a proper, quantitative inspection of the cost-improvement trade-off, nor has the process of acquiring sensors and extracting valuable information from their captured data formalized in a series of methodological steps. This paper introduces the Analyze, Sense, Preprocess, Predict, Implement and Deploy (ASPPID) methodology, an iterative decision workflow that spans from the acquisition of sensing equipment to the quantitative assessment of the contribution of their captured data to enhance the production step under focus. By placing the data scientist at the core of the workflow, this methodology helps improvement teams make informed decisions about which parts of the process need to be sensed, and how to exploit this information towards a verifiable improvement of the production cycle. The implementation of this methodology is exemplified in a real use case within the automotive industry, where the detection of defects in an annealing process can be modeled as a classification problem over a highly imbalanced dataset. Results obtained after applying the proposed ASPPID methodology show that the scrap ratio is reduced by sensing the correct part of the process at minimal investment costs, thus highlighting the crucial role of the data scientist in the management team of manufacturing plants.
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    Bio-inspired optimization for the molecular docking problem: State of the art, recent results and perspectives
    (2019-06) García-Godoy, María Jesús; López-Camacho, Esteban; García-Nieto, José; Del Ser, Javier; Nebro, Antonio J.; Aldana-Montes, José F.; IA
    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 field of single- and multi-objective meta-heuristics for efficiently addressing molecular docking optimization problems. We comprehensively analyze both problem formulations and applied techniques from Evolutionary Computation and Swarm Intelligence, jointly referred to as Bio-inspired Optimization. Our prospective analysis is supported by an experimental study dealing with a molecular docking problem driven by three conflicting objectives, which is tackled by using different multi-objective heuristics. We conclude that genetic algorithms are the most widely used techniques by far, with a noted increasing prevalence of particle swarm optimization in the last years, being these last techniques particularly adequate when dealing with multi-objective formulations of molecular docking problems. We end this experimental survey by outlining future research paths that should be under target in this vibrant area.
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    Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives: A Critical Survey, Results, and Perspectives
    (2022-01-29) Para, Jesus; Del Ser, Javier; Nebro, Antonio J.; IA
    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.
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    Extending the speed-constrained multi-objective PSO (SMPSO) with reference point based preference articulation
    (Springer Verlag, 2018) Nebro, Antonio J.; Durillo, Juan J.; García-Nieto, José; Barba-González, Cristóbal; Del Ser, Javier; Coello Coello, Carlos A.; Benítez-Hidalgo, Antonio; Aldana-Montes, José F.; Fonseca, Carlos M.; Lourenco, Nuno; Machado, Penousal; Paquete, Luis; Whitley, Darrell; Auger, Anne; IA
    The Speed-constrained Multi-objective PSO (SMPSO) is an approach featuring an external bounded archive to store non-dominated solutions found during the search and out of which leaders that guide the particles are chosen. Here, we introduce SMPSO/RP, an extension of SMPSO based on the idea of reference point archives. These are external archives with an associated reference point so that only solutions that are dominated by the reference point or that dominate it are considered for their possible addition. SMPSO/RP can manage several reference point archives, so it can effectively be used to focus the search on one or more regions of interest. Furthermore, the algorithm allows interactively changing the reference points during its execution. Additionally, the particles of the swarm can be evaluated in parallel. We compare SMPSO/RP with respect to three other reference point based algorithms. Our results indicate that our proposed approach outperforms the other techniques with respect to which it was compared when solving a variety of problems by selecting both achievable and unachievable reference points. A real-world application related to civil engineering is also included to show up the real applicability of SMPSO/RP.
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    jMetalPy: A Python framework for multi-objective optimization with metaheuristics: A Python framework for multi-objective optimization with metaheuristics
    (2019-12) Benítez-Hidalgo, Antonio; Nebro, Antonio J.; García-Nieto, José; Oregi, Izaskun; Del Ser, Javier; Quantum; IA
    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 language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.
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    MO-MFCGA: Multiobjective Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
    (Institute of Electrical and Electronics Engineers Inc., 2021) Osaba, Eneko; Del Ser, Javier; Martinez, Aritz D.; Lobo, Jesus L.; Nebro, Antonio J.; Yang, Xin She; Quantum; IA
    Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scientific community. Methods coming from evolutionary computation have shown a remarkable performance for solving this kind of optimization problems thanks to their implicit parallelism and the simultaneous convergence towards the Pareto front. In any case, the resolution of multiobjective optimization problems (MOPs) from the perspective of multitasking optimization remains almost unexplored. Multitasking is an incipient research stream which explores how multiple optimization problems can be simultaneously addressed by performing a single search process. The main motivation behind this solving paradigm is to exploit the synergies between the different problems (or tasks) being optimized. Going deeper, we resort in this paper to the also recent paradigm Evolutionary Multitasking (EM). We introduce the adaptation of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) for solving MOPs, giving rise to the Multiobjective MFCGA (MO-MFCGA). An extensive performance analysis is conducted using the Multiobjective Multifactorial Evolutionary Algorithm as comparison baseline. The experimentation is conducted over 10 multitasking setups, using the Multiobjective Euclidean Traveling Salesman Problem as benchmarking problem. We also perform a deep analysis on the genetic transferability among the problem instances employed, using the synergies among tasks aroused along the MO-MFCGA search procedure.
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    Multi-objective design of time-constrained bike routes using bio-inspired meta-heuristics
    (Springer Verlag, 2018) Osaba, Eneko; Del Ser, Javier; Bilbao, Miren Nekane; Lopez-Garcia, Pedro; Nebro, Antonio J.; Melab, Nouredine; Korosec, Peter; Talbi, El-Ghazali; Quantum; IA
    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 the different street segments encompassing the route and their average road velocity. Additionally, an upper and lower restriction is imposed on the time taken to traverse the route, so that the overall system can be utilized for planning bike rides during free leisure time gaps. Instead of designing a discrete route encoding strategy suitable for heuristic operators, this work leverages a proxy software – Open Trip Planner, OTP – capable of computing routes based on three user-level preference factors (i.e. safety, inclination and duration), which eases the adoption of off-the-shelf multi-objective solvers. The system has been assessed in a realistic simulation environments over the city of Bilbao (Spain) using multi-objective bio-inspired approaches. The obtained results are promising, with route sets trading differently distance for safety of utmost utility for bike users to exploit fully their leisure time.
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    Multi-Objective Optimization of Bike Routes for Last-Mile Package Delivery with Drop-Offs
    (Institute of Electrical and Electronics Engineers Inc., 2018-12-07) Osaba, Eneko; Del Ser, Javier; Nebro, Antonio J.; Laña, Ibai; Bilbao, Miren Nekane; Sanchez-Medina, Javier J.; Quantum; IA
    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 problem, balancing three conflicting objectives: a weighted measure of distance of the route, the safety of the biker, and the economic profit yielded by the delivery of goods to customers. Six different and heterogeneous multi-objective algorithms have been applied to the modeled problem: NSGA-II, MOCell, SMPSO, MOEA/D, NSGA-III and MOMBI2. In order to evaluate the performance of these algorithms, we have devised three experimental setups encompassing different real localizations in Madrid (Spain). For deploying a realistic simulation platform, the open-source Open Trip Planner framework has been used as a proxy evaluator of the produced routes. Results have been compared using the obtained Median and Inter Quartile Range of the hypervolume values reached by the algorithms. Conclusions drawn from this study show that MOCell is the best method for the proposed problem, reaching routes that balance the considered three objectives in a more Pareto-optimal fashion than the rest of counterparts in the benchmark.
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    Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm
    (Springer Science and Business Media Deutschland GmbH, 2023) Para, Jesús; Del Ser, Javier; Nebro, Antonio J.; Dorronsoro, Bernabé; Chicano, Francisco; Danoy, Gregoire; Talbi, El-Ghazali; IA
    In this work we focus on optimizing a multi-objective formulation of the Job Shop Scheduling Problem (JSP) which considers the minimization of energy consumption as one of the objectives. In practice, users experts in the problem domain but with a low knowledge in metaheuristics usually take an existing algorithm with default settings to optimize problem instances but, in this context, the use of automatic parameter configuration techniques can help to find ad-hoc configurations of algorithms that effectively solve optimization problems. Our aim is to study what improvement in results can be obtained by applying an autoconfiguration approach versus using a set of well-known multi-objective evolutionary algorithms (NSGA-II, SPEA2, SMS-EMOA and MOEA/D) for different instances of the JSP, with varying dimensionality. Our experiments showcase the potential of automated algorithmic configuration for energy-efficient production scheduling, producing better balanced solutions than the multi-objective solvers considered in the study.
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    A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems
    (2021-07) Osaba, Eneko; Villar-Rodriguez, Esther; Del Ser, Javier; Nebro, Antonio J.; Molina, Daniel; LaTorre, Antonio; Suganthan, Ponnuthurai N.; Coello Coello, Carlos A.; Herrera, Francisco; Quantum; IA
    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.