Browsing by Keyword "Bio-inspired optimization"
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Item 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.; IAMolecular 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.Item Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations(2020-09-01) Molina, Daniel; Poyatos, Javier; Ser, Javier Del; García, Salvador; Hussain, Amir; Herrera, Francisco; IAIn recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Item Computing rational border curves of melanoma and other skin lesions from medical images with bat algorithm(Association for Computing Machinery, Inc, 2019-07-13) Gálvez, Akemi; Fister, Iztok; Fister, Iztok; Del Ser, Javier; Osaba, Eneko; Iglesias, Andrés; QuantumBorder detection of melanoma and other skin lesions from images is an important step in the medical image processing pipeline. Although this task is typically carried out manually by the dermatologists, some recent papers have applied evolutionary computation techniques to automate this process. However, these works are only focused on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bézier curves by applying the bat algorithm, a popular bio-inspired swarm intelligence technique for optimization. Experimental results on two examples of medical images of melanomas show that this method is promising, as it outperforms the polynomial approach and can be applied to medical images without further pre/post-processing.Item NATURE- AND BIO-INSPIRED OPTIMIZATION: THE GOOD, THE BAD, THE UGLY AND THE HOPEFUL: Lo bueno, lo malo, lo feo y lo esperanzador(2022-03) Molina, Daniel; Poyatos, Javier; Osaba, Eneko; Del Ser, Javier; Herrera, Francisco; Quantum; IANowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine-tuning system designs considering one or more performance factors. Unfortunately, for many complex problems there is no optimization technique that can achieve the optimum solution in a reasonable computation time. As a result, the optimization process is often done manually. In recent years a myriad of optimization techniques have appeared, all inspired by phenomena observed in nature, such as behavioral patterns in animals (such as the exploration and search for food, moving, hunting, …), physical and chemical processes [1]. These techniques, often referred to as nature- or bio-inspired optimization algorithms, allow users to optimize a problem without requiring special knowledge about it: they only need to be informed about the fitness function to be optimized, and the mechanisms by which new candidate solutions can be produced. Each algorithm defines how existing solutions can be combined and modified to create new ones in an intelligent way to search for the best solution. Although they cannot guarantee that the optimum solution will be eventually achieved, they can automatically yield good solutions in reasonable computation times. These features make bio-inspired optimization proposals a promising research area and a great alternative to optimize complex processes, as has been already showcased in many real-world problems. In this work we present nature- and bio-inspired optimization from a global perspective. We describe techniques falling in this area, their evolution, how they operate, and why they bridge an important gap not covered by previous optimization techniques. On a critical note, we also give a clear view of the current situation in the area, indicating the positive aspects and issues that should be urgently improved. Considering this critical view, we suggest promising trends that we believe will lead us to a brighter future in nature- and bio-inspired optimization, plenty of successful examples of their application to real-world engineering problems. The manuscript is structured as follows: Section 2 describes bio-inspired optimization and exposes the reasons and advantages that make this area interesting from the scientific and practical points of view (focusing on introducing what they are and why they are useful). In Section 3 we examine the exciting panorama of recent applications in which nature- and bio-inspired optimization has become a central technology (the good), the upsurge of novel metaphors for the design of new proposals that do not lead to innovative solutions (the bad), and poor methodological practices that draw misleading conclusions that must be avoided in this field (the ugly). Finally, Section 4 summarizes the paper and highlights what is next to be done in the area of bio-inspired optimization (the hopeful), especially for engineering applications.Item On the application of bio-inspired heuristics for network routing with multiple QoS constraints(2017) Bilbao, Miren Nekane; Perfecto, Cristina; Del Ser, Javier; Landa, Xabier; IASince the advent of Telecommunication networks in the early 60’s,routing has become a recurrent problem with evergrowing complexity due to the simultaneous share of resources,stringent Quality of Service (QoS) constraints and unmanageable network scales (size,speed and exchanged data volume) by conventional route finding schemes. This paper considers a particular class of routing problems where the route to be found needs to simultaneously fulfill different requirements in terms of e.g. maximum latency,loss rate or any other cost measure. The manuscript delves into the application of the Coral Reefs Optimization and the Firefly Algorithm,two of the latest bio-inspired meta-heuristic techniques reported to outperform other approximative solvers in a wide range of optimization scenarios. Results obtained from Monte Carlo simulations over synthetic network instances will shed light on the comparative performance of these two algorithms,with emphasis on their convergence speed and statistical significance.Item A prescription of methodological guidelines for comparing bio-inspired optimization algorithms(2021-12) LaTorre, Antonio; Molina, Daniel; Osaba, Eneko; Poyatos, Javier; Del Ser, Javier; Herrera, Francisco; Quantum; IABio-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.