A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector

YesThe prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement.EU FP7 project Policy Compass (Project No. 612133

Deriving Protein Structures Efficiently by Integrating Experimental Data into Biomolecular Simulations

Proteine sind molekulare Nanomaschinen in biologischen Zellen. Sie sind wesentliche Bausteine aller bekannten Lebensformen, von Einzellern bis hin zu Menschen, und erfüllen vielfältige Funktionen, wie beispielsweise den Sauerstofftransport im Blut oder als Bestandteil von Haaren. Störungen ihrer physiologischen Funktion können jedoch schwere degenerative Krankheiten wie Alzheimer und Parkinson verursachen. Die Entwicklung wirksamer Therapien für solche Proteinfehlfaltungserkrankungen erfordert ein tiefgreifendes Verständnis der molekularen Struktur und Dynamik von Proteinen. Da Proteine aufgrund ihrer lichtmikroskopisch nicht mehr auflösbaren Größe nur indirekt beobachtet werden können, sind experimentelle Strukturdaten meist uneindeutig. Dieses Problem lässt sich in silico mittels physikalischer Modellierung biomolekularer Dynamik lösen. In diesem Feld haben sich datengestützte Molekulardynamiksimulationen als neues Paradigma für das Zusammenfügen der einzelnen Datenbausteine zu einem schlüssigen Gesamtbild der enkodierten Proteinstruktur etabliert. Die Strukturdaten werden dabei als integraler Bestandteil in ein physikbasiertes Modell eingebunden. In dieser Arbeit untersuche ich, wie sogenannte strukturbasierte Modelle verwendet werden können, um mehrdeutige Strukturdaten zu komplementieren und die enthaltenen Informationen zu extrahieren. Diese Modelle liefern eine effiziente Beschreibung der aus der evolutionär optimierten nativen Struktur eines Proteins resultierenden Dynamik. Mithilfe meiner systematischen Simulationsmethode XSBM können biologische Kleinwinkelröntgenstreudaten mit möglichst geringem Rechenaufwand als physikalische Proteinstrukturen interpretiert werden. Die Funktionalität solcher datengestützten Methoden hängt stark von den verwendeten Simulationsparametern ab. Eine große Herausforderung besteht darin, experimentelle Informationen und theoretisches Wissen in geeigneter Weise relativ zueinander zu gewichten. In dieser Arbeit zeige ich, wie die entsprechenden Simulationsparameterräume mit Computational-Intelligence-Verfahren effizient erkundet und funktionale Parameter ausgewählt werden können, um die Leistungsfähigkeit komplexer physikbasierter Simulationstechniken zu optimieren. Ich präsentiere FLAPS, eine datengetriebene metaheuristische Optimierungsmethode zur vollautomatischen, reproduzierbaren Parametersuche für biomolekulare Simulationen. FLAPS ist ein adaptiver partikelschwarmbasierter Algorithmus inspiriert vom Verhalten natürlicher Vogel- und Fischschwärme, der das Problem der relativen Gewichtung verschiedener Kriterien in der multivariaten Optimierung generell lösen kann. Neben massiven Fortschritten in der Verwendung von künstlichen Intelligenzen zur Proteinstrukturvorhersage ermöglichen leistungsoptimierte datengestützte Simulationen detaillierte Einblicke in die komplexe Beziehung von biomolekularer Struktur, Dynamik und Funktion. Solche computergestützten Methoden können Zusammenhänge zwischen den einzelnen Puzzleteilen experimenteller Strukturinformationen herstellen und so unser Verständnis von Proteinen als den Grundbausteinen des Lebens vertiefen

Business intelligence in risk management: Some recent progresses

Risk management has become a vital topic both in academia and practice during the past several decades. Most business intelligence tools have been used to enhance risk management, and the risk management tools have benefited from business intelligence approaches. This introductory article provides a review of the state-of-the-art research in business intelligence in risk management, and of the work that has been accepted for publication in this issue of Information Sciences

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

This 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.This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain)

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations