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evolutionary algorithm using conditional expectation value for quadratic assignment problem

SWARM AND evolutionary COMPUTATION 2019年 46卷 1-27页

作者： Chmiel, Wojciech AGH Univ Sci & Technol Fac Elect Engn Automat Comp Sci & Biomed Engn Al Mickiewicza 30 PL-30059 Krakow Poland

This paper is devoted to the implementation of the method based on the theoretical properties of the quadratic assignment problem in the implementation of the nature-inspired algorithms. The equation describing the conditional expectation value for the QAP problem objective function was proposed and proved. The conditional expectation was used in the implementation of several types of pseudo-genetic operators. The results obtained with the use of new operators were compared with the results obtained with the use of standard pseudo-genetic operators for the QAP problem.

关键词： evolutionary computing Quadratic assignment problem Conditional expectation value Pseudo-genetic operators Genetic algorithms

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Efficient financial time series prediction with evolutionary virtual data position exploration

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NEURAL computing & APPLICATIONS 2019年第2-Sup期31卷 1053-1074页

作者： Nayak, Sarat Chandra Misra, Bijan Bihari Behera, Himansu Sekhar Kommuri Pratap Reddy Inst Technol Dept Comp Sci & Engn Hyderabad 500088 Telangana India Silicon Inst Technol Dept Informat Technol Bhubaneswar 751024 Odisha India Veer Surendra Sai Univ Technol Dept Comp Sci Engn & Informat Technol Burla 768018 India

Prediction of stock index remains a challenging task of the financial time series prediction process. Random fluctuations in the stock index make it difficult to predict. Usually the time series prediction is based on the observations of past trend over a period of time. In general, the curve the time series data follows has a linear part and a non-linear part. Prediction of the linear part with past history is not a difficult task, but the prediction of non linear segments is difficult. Though different non-linear prediction models are in use, but their prediction accuracy does not improve beyond a certain level. It is observed that close enough data positions are more informative where as far away data positions mislead prediction of such non linear segments. Apart from the existing data positions, exploration of few more close enough data positions enhance the prediction accuracy of the non-linear segments significantly. In this study, an evolutionary virtual data position (EVDP) exploration method for financial time series is proposed. It uses multilayer perceptron and genetic algorithm to build this model. Performance of the proposed model is compared with three deterministic methods such as linear, Lagrange and Taylor interpolation as well as two stochastic methods such as Uniform and Gaussian method. Ten different stock indices from across the globe are used for this experiment and it is observed that in majority of the cases performance of the proposed EVDP exploration method is better. Some stylized facts exhibited by the financial time series are also documented.

关键词： Virtual data position Financial time series evolutionary computing Multilayer perceptron Deterministic interpolation Stochastic interpolation Genetic algorithm Stylized facts

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Meta-heuristic algorithms to truss optimization: Literature mapping and application

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115197-115197页

作者： Renkavieski, Christopher Parpinelli, Rafael Stubs Santa Catarina State Univ Joinville Grad Program Appl Comp Joinville Brazil

Truss optimization is an important class of engineering problems, with better solutions allowing for reduced material use and reduced construction costs. Due to its complexity, meta-heuristic algorithms are often applied to this problem, since these algorithms require no prior knowledge of the search space, being built upon stochastic rules. The present work has two main objectives: the elaboration of a systematic literature mapping on meta-heuristic algorithms applied to truss optimization, and the application of EB-A-SHADE, a self-adaptive algo-rithm based on Differential Evolution, to truss size optimization with continuous variables. The systematic mapping was made based on 179 articles published between 2010 and 2020 and analyzed the categories of truss optimization, the algorithms used, and parameter self-adaptation techniques. This mapping provided a snapshot of the current state of truss optimization research, showing that it is an active field, with an increasing number of publications each year, but that also includes gaps for potential future works. For the truss size optimization application, seven benchmark structures were used to evaluate the EB-A-SHADE algorithm, and the results ob-tained were compared to those of state-of-the-art algorithms with the use of statistical analysis. Convergence, constraints, and diversity plots were also employed to analyze the algorithm behavior on the problems studied, showing a stable convergence. The experiments showed that EB-A-SHADE achieved competitive results, having the statistical best results in most structures, and with the lowest standard deviation values. These results show that the algorithm can be applied to more complex approaches to the problem at hand.

关键词： Meta-heuristic Algorithms evolutionary computing Differential evolution Truss optimization Literature mapping

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Integration of Classical Mathematical Modeling with an Artificial Neural Network for the Problems with Limited Dataset

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ENERGIES 2021年第16期14卷 5127页

作者： Buchaniec, Szymon Gnatowski, Marek Brus, Grzegorz AGH Univ Sci & Technol 30 Mickiewicza Ave PL-30059 Krakow Poland

One of the most common problems in science is to investigate a function describing a system. When the estimate is made based on a classical mathematical model (white-box), the function is obtained throughout solving a differential equation. Alternatively, the prediction can be made by an artificial neural network (black-box) based on trends found in past data. Both approaches have their advantages and disadvantages. Mathematical models were seen as more trustworthy as their prediction is based on the laws of physics expressed in the form of mathematical equations. However, the majority of existing mathematical models include different empirical parameters, and both approaches inherit inevitable experimental errors. Simultaneously, the approximation of neural networks can reproduce the solution exceptionally well if fed sufficient data. The difference is that an artificial neural network requires big data to build its accurate approximation, whereas a typical mathematical model needs several data points to estimate an empirical constant. Therefore, the common problem that developers meet is the inaccuracy of mathematical models and artificial neural networks. Another common challenge is the mathematical models' computational complexity or lack of data for a sufficient precision of the artificial neural networks. Here we analyze a grey-box solution in which an artificial neural network predicts just a part of the mathematical model, and its weights are adjusted based on the mathematical model's output using the evolutionary approach to avoid overfitting. The performance of the grey-box model is statistically compared to a Dense Neural Network on benchmarking functions. With the use of Shaffer procedure, it was shown that the grey-box approach performs exceptionally well when the overall complexity of a problem is properly distributed with the mathematical model and the Artificial Neural Network. The obtained calculation results indicate that such an approach could inc

关键词： grey-box model mathematical model artificial neural networks evolutionary computing solid oxide fuel cell

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Addressing topic modeling with a multi-objective optimization approach based on swarm intelligence

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KNOWLEDGE-BASED SYSTEMS 2021年 225卷 107113-107113页

作者： Gonzalez-Santos, Carlos Vega-Rodriguez, Miguel A. Perez, Carlos J. Univ Extremadura Dept Math Campus Univ S-N Caceres 10003 Spain Univ Extremadura Dept Comp & Commun Technol Campus Univ S-N Caceres 10003 Spain

Topic modeling is a growing field within the area of text analysis that extracts underlying topics from document collections. Several objectives can be simultaneously considered when designing an approach for topic modeling. A multi-objective optimization approach based on the swarm intelligence of a bee colony (MOABC, Multi-Objective Artificial Bee Colony) has been designed, implemented, and tested. This new approach has been evaluated by using documents from the Reuters-21578 and TagMyNews datasets. Three objective functions (coherence, coverage, and perplexity) and three multi objective metrics (hypervolume, set coverage, and distance to the ideal point) have been considered in two topic scenarios. Results show that MOABC provides relevant improvements with respect to LDA (Latent Dirichlet Allocation, the most used approach for topic modeling) and MOEA/D (Multi-Objective evolutionary Algorithm based on Decomposition, the only multi-objective approach published to date). This demonstrates that the multi-criteria nature of topic modeling should be exploited with multi-objective optimization approaches. (C) 2021 Elsevier B.V. All rights reserved.Y

关键词： Artificial bee colony evolutionary computing Latent Dirichlet allocation Multi-objective optimization Text analysis Topic modeling

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A DAG-based cloud-fog layer architecture for distributed energy management in smart power grids in the presence of PHEVs

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SUSTAINABLE CITIES AND SOCIETY 2021年 75卷 103335-103335页

作者： Lin, Yubin Cheng, Chenbing Xiao, Fen Alsubhi, Khalid Aljahdali, Hani Moaiteq Abdullah State Grid Fujian Power Econ Res Inst Fuzhou 350012 Peoples R China Minjiang Univ Coll Phys & Informat Engn Fozhou 350108 Peoples R China State Grid Fujian Elect Power Co Ltd Fuzhou 350003 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Rabigh Jeddah Saudi Arabia

In this paper, a new framework based on the directed acyclic graph (DAG) and distributed multi-layer cloud-fog computing to find the optimal energy management of the smart grids, considering high penetration of plug-in hybrid electric vehicles (PHEVs). The presented distributed structure lets neighboring agents make a consensus together. The uncertainties have been modeled according to the Monte Carlo simulations, due to wide usages of diverse renewable energy resources such as photovoltaic panels and wind turbines. Three diverse charging schemes have been considered in the smart grid test system which contains controlled, uncontrolled and smart chargings. The Whale Optimization Algorithm (WOA) has been used to solve the augmented Lagrangian function in each agent. The simulation results are shown that the suggested scheme is effective.

关键词： Machine learning evolutionary computing Smart power grid Plug-in hybrid electric vehicle DAG-based cloud-fog computing

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A non-differential method for solving many-objective optimization problems: An application in IPM motor design

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2020年第Sup1期64卷 S131-S142页

作者： Di Barba, Paolo Mognaschi, Maria Evelina Wiak, Slawomir Univ Pavia Dept Elect Comp & Biomed Engn Via Ferrata 5 I-27100 Pavia Italy Lodz Univ Technol Inst Mechatron & Informat Syst Lodz Poland

Many-objective problems, characterized by a high-dimensional objective space, are a source of challenge for standard methods of multi-objective optimization. An evolutionary algorithm based on the concept of least-conflict solutions is here presented;the shape design of a IPM motor is considered as the case study. In particular, optimization problems dealing with 3 and 4 objective functions have been solved, respectively.

关键词： Many-objective optimization evolutionary computing finite-element analysis inverse magnetostatics permanent-magnet motor

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A novel sophisticated hybrid method for intrusion detection using the artificial immune system

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 58卷

作者： Farzadnia, Ehsan Shirazi, Hossein Nowroozi, Alireza Malek Ashtar Univ Technol Fac Elect & Comp Engn Tehran Iran IRIB Univ Media Engn Dept Tehran Iran

Artificial immune system (AIS) as a good prototype for developing Machine Learning (ML) is a promising candidate to design Intrusion Detection System (IDS). Two of its prevalent paradigms, the negative selection, and the danger theory, inspired by immunity responses of the powerful human immune system (HIS) are being widely used in this case. In this paper, we proposed a novel sophisticated hybrid method including two defensive lines by using two aforesaid mechanisms. In the proposed method, decentralized cooperation of dendritic cells together with mature detectors acts as a stimulator to generate efficient and accurate detectors and retain memory in the long-term that meant security control via ensuring immunity. Simulating such a system was applicable only by establishing artificial life cycles in the MATLAB environment. Experimental consequences demonstrate that the present schema gradually ameliorates its overall performance during the incipient stages of life cycles and also it is more capable than the previous ones found in the literature.

关键词： evolutionary computing Intrusion detection Artificial life cycle Negative selection algorithm Dendritic cell Polar coordinates

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Artificial intelligence applied to conceptual design. A review of its use in architecture

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AUTOMATION IN CONSTRUCTION 2021年 124卷 103550-103550页

作者： Pena, M. Luz Castro Carballal, Adrian Rodriguez-Fernandez, Nereida Santos, Iria Romero, Juan Univ A Coruna RNASA IMEDIR Lab CITIC Res Ctr Informat & Commun Technol Comp Sci & Informat Technol Dept ESCI Campus Elvina La Coruna 15008 Spain

Conceptual architectural design is a complex process that draws on past experience and creativity to generate new designs. The application of artificial intelligence to this process should not be oriented toward finding a solution in a defined search space since the design requirements are not yet well defined in the conceptual stage. Instead, this process should be considered as an exploration of the requirements, as well as of possible solutions to meet those requirements. This work offers a tour of major research projects that apply artificial intelligence solutions to architectural conceptual design. We examine several approaches, but most of the work focuses on the use of evolutionary computing to perform these tasks. We note a marked increase in the number of papers in recent years, especially since 2015. Most employ evolutionary computing techniques, including cellular automata. Most initial approaches were oriented toward finding innovative and creative forms, while the latest research focuses on optimizing architectural form.

关键词： Artificial intelligence Architectural conceptual design evolutionary computing Cellular automata

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Risk Identification, Assessments, and Prediction for Mega Construction Projects: A Risk Prediction Paradigm Based on Cross Analytical-Machine Learning Model

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BUILDINGS 2021年第4期11卷 172页

作者： Banerjee Chattapadhyay, Debalina Putta, Jagadeesh Rao, Rama Mohan P. Vellore Inst Technol Sch Civil Engn Vellore 632014 Tamil Nadu India Vellore Inst Technol Ctr Disaster Mitigat & Management Vellore 632014 Tamil Nadu India

Risk identification and management are the two most important parts of construction project management. Better risk management can help in determining the future consequences, but identifying possible risk factors has a direct and indirect impact on the risk management process. In this paper, a risk prediction system based on a cross analytical-machine learning model was developed for construction megaprojects. A total of 63 risk factors pertaining to the cost, time, quality, and scope of the megaproject and primary data were collected from industry experts on a five-point Likert scale. The obtained sample was further processed statistically to generate a significantly large set of features to perform K-means clustering based on high-risk factor and allied sub-risk component identification. Descriptive analysis, followed by the synthetic minority over-sampling technique (SMOTE) and the Wilcoxon rank-sum test was performed to retain the most significant features pertaining to cost, time, quality, and scope. Eventually, unlike classical K-means clustering, a genetic-algorithm-based K-means clustering algorithm (GA-K-means) was applied with dual-objective functions to segment high-risk factors and allied sub-risk components. The proposed model identified different high-risk factors and sub-risk factors, which cumulatively can impact overall performance. Thus, identifying these high-risk factors and corresponding sub-risk components can help stakeholders in achieving project success.

关键词： risk management risk identification construction megaproject quantitative machine learning evolutionary computing

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