285 research outputs found

    Clustering Analysis of the Population in Db_SHADE Algorithm

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    This paper provides an analysis of the population clustering in a novel Success-History based Adaptive Differential Evolution algorithm with Distance based adaptation (Db_SHADE) in order to analyze the exploration and exploitation abilities of the algorithm. The comparison with the original SHADE algorithm is performed on the CEC2015 benchmark set in two dimensional settings (10D and 30D). The clustering analysis helps to answer the question about prolonged exploration phase of the Db_SHADE algorithm. Possible future research directions are drawn in the discussion and conclusion

    Heuristic Approaches to Stochastic Quadratic Assignment Problem: VaR and CVar Cases

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    The goal of this paper is to continue our investigation of the heuristic approaches of solving thestochastic quadratic assignment problem (StoQAP) and provide additional insight into the behavior of di erentformulations that arise through the stochastic nature of the problem. The deterministic Quadratic AssignmentProblem (QAP) belongs to a class of well-known hard combinatorial optimization problems. Working with severalreal-world applications we have found that their QAP parameters can (and should) be considered as stochasticones. Thus, we review the StoQAP as a stochastic program and discuss its suitable deterministic reformulations.The two formulations we are going to investigate include two of the most used risk measures - Value at Risk(VaR) and Conditional Value at Risk (CVaR). The focus is on VaR and CVaR formulations and results of testcomputations for various instances of StoQAP solved by a genetic algorithm, which are presented and discussed

    SOMA Network Model Based on Native Visibility Graph

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    In this article, we want to propose a new model of the network for analyzing the evolution algorithms.We focus on the graph called native visibility graph. We show how we can get a time series from the run ofthe self-organizing migrating algorithm and how we can convert these series into a network. At the end of thearticle, we focus on some basic network properties and we propose how can we use these properties for laterinvestigation. All experiments run on well-known CEC 2016 benchmarks

    Emotion Recognition using AutoEncoders and Convolutional Neural Networks

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    Emotions demonstrate people's reactions to certain stimuli. Facial expression analysis is often used to identify the emotion expressed. Machine learning algorithms combined with artificial intelligence techniques have been developed in order to detect expressions found in multimedia elements, including videos and pictures. Advanced methods to achieve this include the usage of Deep Learning algorithms. The aim of this paper is to analyze the performance of a Convolutional Neural Network which uses AutoEncoder Units for emotion-recognition in human faces. The combination of two Deep Learning techniques boosts the performance of the classification system. 8000 facial expressions from the Radboud Faces Database were used during this research for both training and testing. The outcome showed that five of the eight analyzed emotions presented higher accuracy rates, higher than 90%

    EQUADIFF 15

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    Equadiff 15 – Conference on Differential Equations and Their Applications – is an international conference in the world famous series Equadiff running since 70 years ago. This booklet contains conference materials related with the 15th Equadiff conference in the Czech and Slovak series, which was held in Brno in July 2022. It includes also a brief history of the East and West branches of Equadiff, abstracts of the plenary and invited talks, a detailed program of the conference, the list of participants, and portraits of four Czech and Slovak outstanding mathematicians

    Modeling Complex Systems by Structural Invariants Approach

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    When modeling complex systems, we usually encounter the following difficulties: partiality, large amount of data, and uncertainty of conclusions. It can be said that none of the known approaches solves these difficulties perfectly, especially in cases where we expect emergences in the complex system. The most common is the physical approach, sometimes reinforced by statistical procedures. The physical approach to modeling leads to a complicated description of phenomena associated with a relatively simple geometry. If we assume emergences in the complex system, the physical approach is not appropriate at all. In this article, we apply the approach of structural invariants, which has the opposite properties: a simple description of phenomena associated with a more complicated geometry (in our case pregeometry). It does not require as much data and the calculations are simple. The price paid for the apparent simplicity is a qualitative interpretation of the results, which carries a special type of uncertainty. Attention is mainly focused (in this article) on the invariant matroid and bases of matroid (M, BM) in combination with the Ramsey graph theory. In addition, this article introduces a calculus that describes the emergent phenomenon using two quantities-the power of the emergent phenomenon and the complexity of the structure that is associated with this phenomenon. The developed method is used in the paper for modeling and detecting emergent situations in cases of water floods, traffic jams, and phase transition in chemistry

    Activating supply chain business models' value potentials through Systems Engineering

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    New business opportunities, driven by smart digitalization technology and initiatives such as Industry 4.0, significantly change business models and their innovation rate. The complexity of methodologies developed in recent decades for balancing exploration and exploitation activities of digital transformation has risen. Still, the desired integration levels across organizational levels were often not reached. Systems thinking promises to holistically consider interdisciplinary relationships and objectives of various stakeholders across supply chain ecosystems. Systems theory-based concepts can simultaneously improve value identification and aligned transformation among supply networks' organizational and technical domains. Hence, the study proposes synthesizing management science concepts such as strategic alignment with enterprise architecture concepts and artificial intelligence (AI)-driven business process optimization to increase innovation productivity and master the increasing rate of business dynamics at the same time. Based on a critical review, the study explores concepts for innovation, transformation, and alignment in the context of Industry 4.0. The essence has been compiled into a systems engineering-driven framework for agile value generation on operational processes and high-order capability levels. The approach improves visibility for orchestrating sustainable value flows and transformation activities by considering the ambidexterity of exploring and exploiting activities and the viability of supply chain systems and sub-systems. Finally, the study demonstrates the need to harmonize these concepts into a concise methodology and taxonomy for digital supply chain engineering.OA-hybri
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