Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

How unconventional chaotic pseudo-random generators influence population diversity in differential evolution

This research focuses on the modern hybridization of the discrete chaotic dynamics and the evolutionary computation. It is aimed at the influence of chaotic sequences on the population diversity as well as at the algorithm performance of the simple parameter adaptive Differential Evolution (DE) strategy: jDE. Experiments are focused on the extensive investigation of totally ten different randomization schemes for the selection of individuals in DE algorithm driven by the default pseudo random generator of Java environment and nine different two-dimensional discrete chaotic systems, as the chaotic pseudo-random number generators. The population diversity and jDE convergence are recorded for 15 test functions from the CEC 2015 benchmark set in 30D. © Springer International Publishing AG, part of Springer Nature 2018.2018/177; IC406; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; 710577, Horizon 2020; CA15140; IGA/CebiaTech/2018/003; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development FundMinistry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST ActionEuropean Cooperation in Science and Technology (COST) [CA15140, IC406]; SGS [2018/177]; VSB-TUO; EU's Horizon 2020 research and innovation programme [710577

On the applicability of random and the best solution driven metaheuristics for analytic programming and time series regression

This paper provides a closer insight into applicability and performance of the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. AP can be considered as a robust open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving metaheuristic. The motivation behind this research is to explore and investigate the applicability and differences in performance of AP driven by basic canonical entirely random or best solution driven mutation strategies of DE. An experiment with four case studies has been carried out here with the several time series consisting of GBP/USD exchange rate. The differences between regression/prediction models synthesized using AP as a direct consequence of different DE strategies performances are statistically compared and briefly discussed in conclusion section of this paper. © 2019, Springer International Publishing AG, part of Springer Nature.CA15140, COST, European Cooperation in Science and Technology; IC406, COST, European Cooperation in Science and Technology; IGA/CebiaTech/2018/003; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development Fund; COST, European Cooperation in Science and TechnologyMinistry of Education, Youth and Sports of the Czech Republic [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [CA15140, IC406

Is chaotic randomization advantageous for higher dimensional optimization problems?

The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports analysis on the performance of popular and CEC 2019 competition winning strategy of Differential Evolution (DE), which is jDE, for optimization problems of higher dimensions. Experiments utilize ten chaos-driven quasi-random schemes for the indices selection and chaotic-driven crossover operations in the DE. All important performance characteristics are recorded and analyzed with simple descriptive statistics, Friedman rank tests and target-based comparisons analyzing distribution of hitting p% best minimum values over all versions and runs of jDE. The test suite was CEC 2015 in 50D. © 2020, Springer Nature Switzerland AG

Performance testing of multi-chaotic differential evolution concept on shifted benchmark functions

This research deals with the hybridization of the two softcomputing fields, which are chaos theory and evolutionary computation. This paper aims on the investigations on the multi-chaos-driven evolutionary algorithm Differential Evolution (DE) concept. This paper is aimed at the embedding and alternating of set of two discrete dissipative chaotic systems in the form of chaos pseudo random number generators for the DE. In this paper the novel initial concept of DE/rand/1/bin strategy driven alternately by two chaotic maps (systems) is introduced. From the previous research, it follows that very promising results were obtained through the utilization of different chaotic maps, which have unique properties with connection to DE. The idea is then to connect these two different influences to the performance of DE into the one multi-chaotic concept. Repeated simulations were performed on the selected set of shifted benchmark functions in higher dimensions. Finally, the obtained results are compared with canonical DE

An algorithm for swarm robot to avoid multiple dynamic obstacles and to catch the moving target

This paper presents a method for swarm robot to catch the moving target and to avoid multiple dynamic obstacles in the unknown environment. An imaginary map is built, including the highest mountain, some small hills, and a lowest lying land, respectively corresponding to the starting position of the robot, the detected obstacles, and the target. The robot is considered as a flow of water flowing from high to low. The flow of water is the robot trajectory that is divided into a set of points created by an algorithm called Self-organizing migrating algorithm. Simulation results are also presented to show that the obstacle avoidance and catching target task can be reached using this method. © Springer Nature Switzerland AG 2019.VSB-Technical University of Ostrava [SGS 2019/137]; Ministry of Education, Youth and Sports of the Czech RepublicMinistry of Education, Youth & Sports - Czech Republic [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089