Browsing by Keyword "Swarm intelligence"
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Item Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions: state of the art and future directions(2021-08-03) Torre-Bastida, Ana I.; Díaz-de-Arcaya, Josu; Osaba, Eneko; Muhammad, Khan; Camacho, David; Del Ser, Javier; HPA; QuantumThis overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.Item Bio-inspired computation: Where we stand and what's next(2019-08) Del Ser, Javier; Osaba, Eneko; Molina, Daniel; Yang, Xin She; Salcedo-Sanz, Sancho; Camacho, David; Das, Swagatam; Suganthan, Ponnuthurai N.; Coello Coello, Carlos A.; Herrera, Francisco; IA; QuantumIn recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.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 Community detection in networks using bio-inspired optimization: Latest developments, new results and perspectives with a selection of recent meta-heuristics(2020-02) Osaba, Eneko; Del Ser, Javier; Camacho, David; Bilbao, Miren Nekane; Yang, Xin She; Quantum; IADetecting 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.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 Cuckoo search algorithm for border reconstruction of medical images with rational curves(Springer Verlag, 2019) Gálvez, Akemi; Fister, Iztok; Fister, Iztok; Osaba, Eneko; Ser, Javier Del; Iglesias, Andrés; Tan, Ying; Shi, Yuhui; Niu, Ben; Quantum; IABorder 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.Item Interplay of two bat algorithm robotic swarms in non-cooperative target point search(Springer Verlag, 2018) Suárez, Patricia; Gálvez, Akemi; Fister, Iztok; Osaba, Eneko; Del Ser, Javier; Iglesias, Andrés; Corchado, Juan M.; Julian, Vicente; Osaba Icedo, Eneko; Bajo, Javier; Hoffa-Dabrowska, Patrycja; Silveira, Ricardo Azambuja; Fernandez, Alberto; Giroux, Sylvain; Navarro Martínez, Elena María; Mathieu, Philippe; Castro, Antonio J.; Sanchez-Pi, Nayat; del Val, Elena; Unland, Rainer; Fuentes-Fernandez, Ruben; Quantum; IAIn this paper, we analyze the interplay of two robotic swarms applied to solve a target point search in a non-cooperative mode. In particular, we consider the case of two identical robotic swarms deployed within the same environment to perform dynamic exploration seeking for two different unknown target points. It is assumed that the environment is unknown and completely dark, so no vision sensors can be used. Our work is based on a robotic swarm approach recently reported in the literature. In that approach, the robotic units are driven by a popular swarm intelligence technique called bat algorithm. This technique is based on echolocation with ultrasounds, so it is particularly well suited for our problem. The paper discusses the main findings of our computational experiments through three illustrative videos of single executions.Item Layout optimisation of wave energy converter arrays(2017) Mercadé Ruiz, Pau; Nava, Vincenzo; Topper, Mathew B.R.; Minguela, Pablo Ruiz; Ferri, Francesco; Kofoed, Jens Peter; RENOVABLES OFFSHOREThis paper proposes an optimisation strategy for the layout design of wave energy converter (WEC) arrays. Optimal layouts are sought so as to maximise the absorbed power given a minimum q-factor, the minimum distance between WECs, and an area of deployment. To guarantee an efficient optimisation, a four-parameter layout description is proposed. Three different optimisation algorithms are further compared in terms of performance and computational cost. These are the covariance matrix adaptation evolution strategy (CMA), a genetic algorithm (GA) and the glowworm swarm optimisation (GSO) algorithm. The results show slightly higher performances for the latter two algorithms; however, the first turns out to be significantly less computationally demanding.Item Memetic modified cuckoo search algorithm with ASSRS for the SSCF problem in self-similar fractal image reconstruction(Springer Verlag, 2018) Gálvez, Akemi; Iglesias, Andrés; Fister, Iztok; Fister, Iztok; Osaba, Eneko; Del Ser, Javier; Herrero, Alvaro; Quintian, Hector; Antonio Saez, Jose; Corchado, Emilio; de Cos Juez, Francisco Javier; Villar, Jose Ramon; de la Cal, Enrique A.; Quantum; IAThis paper proposes a new memetic approach to address the problem of obtaining the optimal set of individual Self-Similar Contractive Functions (SSCF) for the reconstruction of self-similar binary IFS fractal images, the so-called SSCF problem. This memetic approach is based on the hybridization of the modified cuckoo search method for global optimization with a new strategy for the Lévy flight step size (MMCS) and the adaptive step size random search (ASSRS) heuristics for local search. This new method is applied to some illustrative examples of self-similar fractal images with satisfactory graphical and numerical results. Our approach represents a substantial improvement with respect to a previous method based on the original cuckoo search algorithm for all contractive functions of the examples in this paper.Item Novelty search for global optimization(2019-04-15) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Fister, Iztok; Perc, Matjaž; Slavinec, Mitja; IA; QuantumNovelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k-nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization.