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Evaluation of conjugate depths of hydraulic jump in circular pipes using evolutionary computing

SOFT computing 2019年第24期23卷 13375-13391页

作者： Najafzadeh, Mohammad Grad Univ Adv Technol Fac Civil & Surveying Engn Dept Water Engn POB 76315-116 Kerman Iran

Hydraulic jump phenomenon occurring in the circular pipes is a complex issue which has been paid studious attentions by hydraulic engineers. Several experimental and numerical studies were performed so as to characterize hydraulic jump in circular pipes. There are few comprehensive equations to predict the conjugate depth in circular pipes. In this investigation, three numerical models on the basis of evolutionary computing as gene-expression programming (GEP), model tree (MT), and evolutionary polynomial regression (EPR) have been utilized to evaluate the conjugate depths of the hydraulic jump in the circular pipes. Two non-dimensional parameters were yielded from conceptions of specific force to determine a functional relationship between the input and output variables. The performances of proposed approaches were compared with those obtained using conventional methods. The performance of MT indicated an accurate prediction of conjugate depths (R=0.995 and RMSE=0.023) in comparison with those other artificial intelligence (AI) models and empirical equations. The uncertainties prediction of the improved models were quantified and compared with those of existing models. The results of the proposed models demonstrated that linear equations provided by MT had more convenient application than empirical equations extracted from experimental investigations.

关键词： Hydraulic jump evolutionary computing Model classification Conjugate depth Circular pipe Specific force equation

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Optimizing group learning: An evolutionary computing approach

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ARTIFICIAL INTELLIGENCE 2019年 275卷 235-251页

作者： Douven, Igor Sorbonne Univ CNRS SND Paris France

We study groups of interacting agents who are trying to discover probabilistic truths on the basis of sequentially provided evidence and information about the belief states of other group members. The main research question is which combination of epistemic principles- combinations of an evidential update rule, a rule for determining peerhood, and a rule for aggregating probability functions-such groups should adopt to strike the best balance between being fast and being accurate, where the former is understood by reference to how long it takes before a majority of the group assigns a high probability to the true hypothesis, and the latter by reference to the average Brier penalty incurred by the group. The main methodology to be used is that of agent-based optimization, which is a specific form of evolutionary computing. We implement this methodology in a generalization of the Hegselmann-Krause model. In the end, we are able to identify optimal procedures for taking into account both direct evidence and information about one's peers' beliefs. At the same, we note that optimality for such procedures is dependent on context. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Agent-based modeling Computer simulations Explanatory reasoning evolutionary computing Group learning Hegselmann-Krause model Opinion dynamics

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Enhanced evolutionary computing based artificial intelligence model for web-solutions software reusability estimation

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S9787-S9804页

作者： Padhy, Neelamdhab Singh, R. P. Satapathy, Suresh Chandra SSSUTM Dept Comp Sci & Engn Sehore India SSSUTM Dept Elect & Commun Engn Sehore India PVP Siddhartha Inst Technol Dept Comp Sci & Engn Vijayawada India

Ensuring the aging resilient software design can be of paramount significance to enable faultless software system. Particularly assessing reusability extent of the software components can enable efficient software design. The probability of aging proneness can be characterized based on key OO-SM like cohesion, coupling and complexity of a software component. In this paper, aging resilient software reusability prediction model is proposed for object oriented design based Web of Service (WoS) software systems. This work introduces multilevel optimization to accomplish a novel reusability prediction model. Considering coupling, cohesion and complexity as the software characteristics to signify aging proneness, six CK metrics;WMC, CBO, DIT, LCOM, NOC, and RFC are obtained from 100 WoS software. The extracted CK metrics are processed for min-max normalization that alleviates data-unbalancing and hence avoids saturation during learning. The 10-fold Cross-validation followed by outlier detection is considered to enrich data quality for further feature extraction. To reduce computational overheads RSA algorithm is applied. SoftAudit tool is applied to estimate reusability of each class, while binary ULR estimates calculates (reuse proneness) threshold. Applying different classification algorithms such as LM, ANN algorithms, ELM, and evolutionary computing enriched ANN reuse-proneness prediction has been done. The performance assessment affirms that AGA based ANN model outperforms other techniques and hence can be used for earlier aging-resilient reusability optimization for WoS software design.

关键词： Web of Service Aging-resilient Software reusability prediction evolutionary computing ANN

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Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

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AD HOC NETWORKS 2020年 101卷 102094-000页

作者： Zhang, Xiu Lu, Xiaohui Zhang, Xin Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China

Due to the continuous development and progress of wireless communication technology and sensor network technology, wireless sensor networks (WSNs) have gradually become an attractive technology that facilitates people's lives. Due to the extensive use of WSNs, maximizing the lifetime of WSNs to obtain real-time and effective information has become a critical concern. This paper studies the life of mobile wireless sensor networks (MWSNs). MWSNs are a special type of WSN in that the sensor nodes are movable within a certain area. A system model is developed to prolong the network lifetime of MWSNs. This paper uses five evolutionary computing (EC) algorithms to develop the MWSN lifetime optimization model. Numerical simulations are performed to study the advantages and disadvantages of the five algorithms for solving the model. The comparison and discussion can provide advice for using EC algorithms to solve MWSN lifetime maximization problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Wireless sensor network Lifetime maximization evolutionary computing Energy efficient Optimization

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A hybrid optimization approach using evolutionary computing and Map Reduce Architecture

A hybrid optimization approach using Evolutionary Computing ...

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International Conference on Advances in computing and Communication Engineering (ICACCE)

作者： Lohani, Bhanu Prakash Singh, Ajit Bibhu, Vimal UTU Dept CSE Dehra Dun Uttarakhand India BTKIT Dept CSE Dwarahat Uttarakhand India Amity Univ Gr Noida Dept CSE Greater Noida Uttar Pradesh India

ISBN: (纸本)9781728132501

Big data and its application are very popular now a day because due to technological advancement in the field of Information technology the amount of data generation rate is too high. Data Mining & analysis is done for making better decision with respect to the data generated from different sources. Decision making for the route in the traffic environment is also a problem of Big Data because it creates a huge amount of data so we need to optimize or seprate the data with respect to various criteria, for this we need to know about the optimization algorithms. evolutionary computing is a branch of Computer science which works upon the concept of Darwinian evolution and the evolutionary computing algorithms are used to find the optimal solution. The review of optimization algorithm is presented in this paper. For the optimization process we have selected Ant Colony optimization algorithm to find the best route and implemented the algorithm using the concept of Map reduce architecture for parallel processing.

关键词： Big Data evolutionary computing Map-Reduce Optimization Parallel Processing Decision making Data Analysis

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Deep Metacyclic Parameter Search: Non-Convex Optimization Based on evolutionary computing with a Few Twists 10

Deep Metacyclic Parameter Search: Non-Convex Optimization Ba...

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10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Csapo, Adam B. Szechenyi Istvan Univ Dept Informat Gyor Hungary

ISBN: (纸本)9781728147932

This paper proposes a new framework for 11011 convex optimization referred to as Metacyclic Parameter Search (MEPS). The framework combines several approaches that are well known from the field of artificial intelligence namely the iterative update of generations of candidate solutions prevalent in evolutionary computing and particle swarm optimization, as well as metacognitive approaches implementing reward-based improvement such as (deep) reinforcment learning resulting in a gradient-free approach to non-convex optimization that combines the benefits (and alleviates the mutual shortcomings) of each of these individual approaches. Following an overview of the framework, its workings are demonstrated on three rudimentary examples.

关键词： Non-convex optimization Gradient-free optimization evolutionary computing Reinforcement learning Spiral Discovery Method

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Dynamic Generation of Internet of Things Organizational Structures Through evolutionary computing

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IEEE INTERNET OF THINGS JOURNAL 2018年第2期5卷 943-954页

作者： Shen, Zhiqi Yu, Han Yu, Ling Miao, Chunyan Chen, Yiqiang Lesser, Victor R. Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Nanyang Technol Univ Joint NTU UBC Res Ctr Excellence Act Living Elder Singapore 639798 Singapore Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Univ Massachusetts Sch Comp Sci Amherst MA 01003 USA

In today's world, intelligent embedded devices and sensors are interconnected into a dynamic and global network infrastructure is referred to as the Internet of Things (IoT). It has been widely recognized that the performance of an IoT is highly affected by how it is organized. A large-scale system may have billions of possible ways of being organized, which makes it impractical to find a high quality choice of organization by manual means. In this paper, we propose a genetic algorithm (GA) aided framework for generating hierarchical IoT organizational structures. We propose a novel unique mapping between organizational structures and genome representations. Since hierarchical (i.e., tree-structured) organizations are one of the most common forms of organizations, we propose a novel method to map the phenotypic hierarchical structure space into a genome-like array representation space. This new representation opens up opportunities for evolutionary computing techniques to help IoT applications automatically generate organizational structures according to desired objective functions. Based on this mapping, we introduce the hierarchical GA which enriches standard genetic programming approaches with the hierarchical crossover operator with a repair strategy and the mutation of small perturbation operator. The proposed approach is evaluated in an IoT-based information retrieval system. The results have shown that competitive baseline structures which lead to IoT organizations with good performance in terms of utility can be found by the proposed approach during the evolutionary search. Compared with the traditional genetic operators, the newly introduced operators produced organizations of higher utility more consistently under a variety of test cases. The proposed approach is computationally efficient in large search spaces and provides a novel method for future generations of IoT systems to autonomously improve performance.

关键词： evolutionary computing Internet of Things (IoT) organization

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evolutionary computing in recommender systems: a review of recent research

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NATURAL computing 2017年第3期16卷 441-462页

作者： Horvath, Tomas de Carvalho, Andre C. P. L. F. Univ Sao Paulo Dept Comp Sci Caixa Postal 668 BR-13560970 Sao Carlos SP Brazil Pavol Jozef Safarik Univ Inst Comp Sci Kosice Slovakia

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.

关键词： evolutionary computing Genetic algorithms Genetic programming Recommender systems Personalization

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Pitch tracking algorithm based on evolutionary computing with regularisation in very low SNR

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1509-1514页

作者： Zhang, Xiaoheng Li, Yongming Chongqing Radio & TV Univ Chongqing 400052 Peoples R China Chongqing Univ Commun Engn Chongqing 400030 Peoples R China

The authors present PTEAR_VLSNR (Pitch Tracking basing on evolutionary Algorithm with Regularization at Very Low SNR), a pitch tracking algorithm for speech in strong noise. The algorithm builds a pitch enhancement and extraction model, which enhance the pitch by a matched filter, and to further deal with strong noise, the optimal factor was proposed, which can be optimised globally by the evolutionary computing. Specially, regularisation constraint of fitness function was applied to enhance the generalisation ability. Temporal dynamics constraints are used to improve the tracking rate and the voicing decision can be optimal by evolutionary computing similarly. In addition, the balance of optimisation accuracy and time cost were considered. In experiments, genetic algorithm and particle swarm optimisation with two-norm term were represented as evolutionary algorithms with regularisation. At last, they compare the performance of the algorithm and other representative algorithms. The experimental results show that this proposed algorithm performs well in both high and low signal-to-noise ratios (SNRs).

关键词： speech processing particle swarm optimisation genetic algorithms target tracking pitch tracking algorithm evolutionary computing pitch enhancement extraction model regularisation constraint tracking rate genetic algorithm evolutionary algorithms representative algorithms very low SNR PTEAR_VLSNR PTEAR_VLSNR matched filter voicing decision particle swarm optimisation two-norm term high signal-to-noise ratios

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Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

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SWARM AND evolutionary COMPUTATION 2017年 32卷 68-84页

作者： Gotmare, Akhilesh Bhattacharjee, Sankha Subhra Patidar, Rohan George, Nithin V. Indian Inst Technol Gandhinagar Dept Elect Engn Gandhinagar 382355 Gujarat India

An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： System identification Filter design Swarm intelligence evolutionary computing Hammerstein model Chaotic system

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