Pairs of disjoint sets (orthopairs) naturally arise or have points in common with many tools to manage uncertainty: rough sets, shadowed sets, version spaces, three-valued logics, etc. Indeed, they can be used to model partial knowledge, borderline cases, consensus, examples and counter-examples pairs. Moreover, generalized versions of orthopairs are the well known theories of Atanassov intuitionistic fuzzy sets and possibility theory and the newly established three-way decision theory. Thus, it is worth studying them on an abstract level in order to outline general properties that can then be casted to the different paradigms they are in connection with. In this paper, we will review how to define orthopairs and a hierarchy on them in the light of granular computing. Aggregation operators will also be discussed as well as possible generalizations and connections with different paradigms. This will permit us to point out new facets of these paradigms and outline some possible future developments.

/pdf/orthopairs-and-granular-computing-1xwujqjlp0.pdf

Orthopairs and granular computing

In this article, we encompass an analysis of the recent advances in parallel genetic algorithms (PGAs). We have selected these algorithms because of the deep interest in many research fields for techniques that can face complex applications where running times and other computational resources are greedily consumed by present solvers, and PGAs act then as efficient procedures that fully use modern computational platforms at the same time that allow the resolution of cutting-edge open problems. We have faced this survey on PGAs with the aim of helping newcomers or busy researchers who want to have a wide vision on the field. Then, we discuss the most well-known models and their implementations from a recent (last six years) and useful point of view: We discuss on highly cited articles, keywords, the venues where they can be found, a very comprehensive (and new) taxonomy covering different research domains involved in PGAs, and a set of recent applications. We also introduce a new vision on open challenges and try to give hints that guide practitioners and specialized researchers. Our conclusion is that there are many advantages to using these techniques and lots of potential interactions to other evolutionary algorithms; as well, we contribute to creating a body of knowledge in PGAs by summarizing them in a structured way, so the reader can find this article useful for practical research, graduate teaching, and as a pedagogical guide to this exciting domain.

Parallel Genetic Algorithms: A Useful Survey

Thermal conductivity, k, is an important property of concrete, and it influences the design and energy-efficiency of many concrete-based structures. Due to the requirement of sophisticated test procedures, experimental measurement of k of concrete for every such structure is impractical, and therefore, a model for prediction of k is demanded. For this purpose, a data-driven machine learning (ML) model was developed in this study. The dataset for model training was developed from the published literature that contained missing data. For the imputation of missing data, fractional hot-deck imputation (FHDI) provided better performance than manual and naive methods and is proposed for the imputation of similar datasets. Evaluation of nine ML algorithms, feature selection, and tuning of hyperparameters led to a final multilayer perceptron (MLP) model with the highest prediction accuracy. The model was developed using the Maxout activation function and three hidden layers, each containing 100 neurons. It performed reasonably well on the training, validation, and an independent dataset with a mean absolute error of 0.07, 0.14, and 0.10 W/m-K, respectively. The developed model was incorporated for a case study on a typical mass concrete mixture. The results indicated that a combination of quartz sand and siltstone could increase k and the rate of cooling and consequently can reduce the probability of thermal cracking in a mass concrete element. The developed model can provide more similar quantitative information that can aid in informed decision-making for the construction of critical structural elements. Besides, the robustness of the model can further be improved by a larger training dataset.

Machine learning based prediction model for thermal conductivity of concrete

Artificial neural networks (ANNs) have achieved significant success in tackling classical and modern machine learning problems. As learning problems grow in scale and complexity, and expand into multi-disciplinary territory, a more modular approach for scaling ANNs will be needed. Modular neural networks (MNNs) are neural networks that embody the concepts and principles of modularity. MNNs adopt a large number of different techniques for achieving modularization. Previous surveys of modularization techniques are relatively scarce in their systematic analysis of MNNs, focusing mostly on empirical comparisons and lacking an extensive taxonomical framework. In this review, we aim to establish a solid taxonomy that captures the essential properties and relationships of the different variants of MNNs. Based on an investigation of the different levels at which modularization techniques act, we attempt to provide a universal and systematic framework for theorists studying MNNs, also trying along the way to emphasise the strengths and weaknesses of different modularization approaches in order to highlight good practices for neural network practitioners.

A review of modularization techniques in artificial neural networks

The housing of totally enclosed fan-cooled electrical machines is usually finned to maximize the heat dissipation over its surface. Complex models involving heat and fluid flow simultaneously are necessary to evaluate a single design. An optimization over a wide parameter range is, therefore, not possible. In this paper, a novel approach on how to model this particular conjugate heat transfer problem in 2-D without the help of lumped circuits is presented. The proposed method is applied to an optimization process controlled by an evolutionary algorithm. Comparative, more detailed simulations in computational fluid dynamic (CFD) verify the tendencies obtained in the optimization. In another case, the method is used to evaluate the allowable degree of engineering tolerance for a given design. Although the method does not provide results as accurate as CFD, it is very helpful to study a large parameter set and determine the CFD candidates.

Cooling Fin Optimization on a TEFC Electrical Machine Housing Using a 2-D Conjugate Heat Transfer Model

Part E: Evolutionary Computation

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Part E: Evolutionary Computation