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Trivariate non-Gaussian copulas to analyze the spatial behavior of rat sightings

STATISTICA NEERLANDICA 2019年第2期73卷 256-273页

作者： Ayyad, C. Mateu, J. Omidi, M. Tamayo-Uria, I. Mohammadzadeh, M. Jaume I Univ Dept Math E-12071 Castellon De La Plana Spain Ilam Univ Dept Math Ilam Iran Ctr Res Environm Epidemiol CREAL Barcelona Spain Tarbiat Modares Univ Dept Stat Tehran Iran

The brown rat lives with man in a wide variety of environmental contexts and adversely affects public health by transmission of diseases, bites, and allergies. Understanding behavioral and spatial correlation aspects of pest species can contribute to their effective management and control. Rat sightings can be described by spatial coordinates in a particular region of interest defining a spatial point pattern. In this paper, we investigate the spatial structure of rat sightings in the Latina district of Madrid (Spain) and its relation to a number of distance-based covariates that relate to the proliferation of rats. Given a number of locations, biologically considered as attractor points, the spatial dependence is modeled by distance-based covariates and angular orientations through copula functions. We build a particular spatial trivariate distribution using univariate margins coming from the covariate information and provide predictive distributions for such distances and angular orientations.

关键词： bees algorithm copula functions multivariate copulas rat sightings spatial copula spatial point patterns Weibull distribution

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Meta-Heuristic Tuning of the LQR Weighting Matrices Using Various Objective Functions on an Experimental Flexible Arm Under the Effects of Disturbance

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第8期46卷 7323-7336页

作者： Bilgic, Hasan Huseyin Sen, Muhammed Arif Yapici, Ahmet Yavuz, Hakan Kalyoncu, Mete Iskenderun Tech Univ Fac Engn & Nat Sci Dept Mech Engn TR-31200 Iskenderun Hatay Turkey Konya Tech Univ Dept Mech Engn Konya Turkey Cukurova Univ Dept Mech Engn Adana Turkey

In this paper, meta-heuristic tuning of LQR weighting matrices using various objective functions on the experimental flexible arm under the effects of disturbance is investigated. The use of flexible and lightweight systems provides certain advantages such as high operating speeds, low electricity consumption, and low initial investment costs. However, such systems are more prone to vibration-related problems than their rigid and heavyweight counterparts. One of the closed-loop control methods used to suppress these vibrations is LQR control, but the success of the controller depends on the choice of the gain and regulator matrices. In this study, the bees algorithm, a meta-heuristic search algorithm, is used to determine LQR matrices. The system performance is compared with similar studies in the literature. The proposed objective function reveals an improvement of 3.1% in rotor maximum overshoot compared to studies in the literature. Flexible link maximum overshoot has been reduced from 17.8 to 7.1%. In order to validate the applicability and repeatability of the proposed method under the noise and disturbance in real systems, experimental verification tests were conducted using the Quanser Flexible Link system. As a result, the proposed method has been experimentally validated and it is estimated that it may well be a very useful controller design approach in various engineering systems and related control system developments.

关键词： bees algorithm LQR Optimization Noise rejection Disturbance rejection Flexible link

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Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

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SN APPLIED SCIENCES 2020年第2期2卷 273页

作者： Ezugwu, Absalom E. Univ KwaZulu Natal Sch Comp Sci King Edward RdPietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be

关键词： Automatic clustering Clustering analysis Differential evolution Particle swarm optimization Firefly algorithm Invasive weed optimization Artificial bee colony bees algorithm Biogeography-based optimization Harmony search Symbiotic organisms search Teaching-learning-based optimization DB validity index CS validity index

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Autonomic Power Aware Cloud Resources Orchestration Architecture for Web Applications

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2013年第6期6卷 63-81页

作者： Darwish, R. R. Helwan Univ Dept Mech Engn Cairo Egypt

Endless resources provisioning illusion is the mainstay for cloud computing paradigm. However, the unpredictable volatility nature involving web applications workload demand would highly hinder cloud computing platforms performance, furthermore, expose cloud resources for possible devastation. Accordingly, this work proposes autonomic power aware SLA-oriented cloud resources orchestration two-tier architecture. Despite complexity and uncertainties of the workload fluctuations, the proposed architecture geared for leveraging cloud system resources utilization, ensuring explicit guarantees on web applications' responsiveness obligations, meanwhile achieving power consumption minimization objectives. The proposed architecture consolidates heuristic methodologies along with control theory approaches in a resource orchestration hierarchical structure. Firstly, an autonomic global controller is presented. The proposed global controller exploits heuristic methodology for mapping virtual machines (VMs) to the appreciate cloud resources in accordance to heuristic multidimensional objectives based placement strategy. Secondly, a proactive fuzzy-logic based local controller is proposed. The proposed local controller aimed at in confronting workloads' sustainable fluctuations via proactive amendment for the placement and provisioning schedules. Furthermore, the proposed local controller oriented towards maintaining active power management policy especially during transient peak of usage, thereby mitigating overall costs, and extending resources capacity and performance capabilities. Simulation results and comparisons demonstrate that the proposed architecture significantly surpasses previous approaches in terms of total energy consumption, furthermore maintaining web applications SLAs objectives despite dynamic workload scenarios.

关键词： bees algorithm Cloud Computing Fuzzy Logic Controller Resource Orchestration Virtualization Web Applications

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Deep Learning: Parameter Optimization Using Proposed Novel Hybrid bees Bayesian Convolutional Neural Network

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APPLIED ARTIFICIAL INTELLIGENCE 2022年第1期36卷 Article: 2031815页

作者： Alamri, Nawaf Mohammad H. Packianather, Michael Bigot, Samuel King Abdulaziz Univ Jeddah Saudi Arabia Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

Deep Learning (DL) is a type of machine learning used to model big data to extract complex relationship as it has the advantage of automatic feature extraction. This paper presents a review on DL showing all its network topologies along with their advantages, limitations, and applications. The most popular Deep Neural Network (DNN) is called a Convolutional Neural Network (CNN), the review found that the most important issue is designing better CNN topology, which needs to be addressed to improve CNN performance further. This paper addresses this problem by proposing a novel nature inspired hybrid algorithm that combines the bees algorithm (BA), which is known to mimic the behavior of honey bees, with Bayesian Optimization (BO) in order to increase the overall performance of CNN, which is referred to as BA-BO-CNN. Applying the hybrid algorithm on Cifar10DataDir benchmark image data yielded an increase in the validation accuracy from 80.72% to 82.22%, while applying it on digits datasets showed the same accuracy as the existing original CNN and BO-CNN, but with an improvement in the computational time by 3 min and 12 s reduction, and finally applying it on concrete cracks images produced almost similar results to existing algorithms.

关键词： Convolutional neural network bees algorithm Hybrid algorithm Deep learning Neural network Bayesian optimization

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Heuristic Optimization Methods Applied to Improve the Performance of Controlled DC-DC High Boost Converter with Coupled Inductors 19

Heuristic Optimization Methods Applied to Improve the Perfor...

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19th International Conference on Electrical Machines and Systems (ICEMS)

作者： Marsala, Giuseppe Ragusa, Antonella UOS Palermo CNR ISSIA Palermo Italy

ISBN: (纸本)9784886860989

This paper presents the optimization of the performances of a controlled zero ripple input current high boost dc-dc converter with coupled inductors. In particular, an optimization of PID and LQR controllers design is presented, using three heuristic methods: Genetic, Particle Swarm and bees algorithms. A comparison between the PID and LQR controllers, with and without optimization, is presented together with the analysis of effectiveness of the three heuristic algorithms to design the control strategies.

关键词： DC-DC converter coupled inductors control strategies genetic algorithm particle swarm optimization bees algorithm

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Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications

Exploring the High Performance Computing-Enablement of a Sui...

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10th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

作者： Li, Zhenya Sinnott, Richard O. Choon, Yee Wen Sjaugi, Muhammad Farhan Mohammad, Mohd Saberi Deris, Safaai Napis, Suhaimi Omatu, Sigeru Manuel Corchado, Juan Ibrahim, Zuwairie Yusof, Zulkifli Md Univ Melbourne Dept Comp & Informat Syst Melbourne Vic Australia Univ Teknol Malaysia Fac Comp Johor Baharu Malaysia Perdana Univ Sch Data Sci Serdang Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Kota Baharu Malaysia Univ Putra Malaysia Fac Biotechnol & Biomol Sci Serdang Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka Japan Univ Salamanca BISITE Res Grp Biomed Res Inst Salamanca Salamanca Spain Univ Malaysia Pahang Fac Mfg Engn Fac Elect & Elect Gambang Malaysia

ISBN: (纸本)9783319401263;9783319401256

Genetic engineering provides methods to modify the genes of microorganisms to achieve desired effects. This can be done for improved organism growth rate or increasing production yield of a desired gene product. Gene knockout is a technique that can improve the specific characteristics of microorganisms by disabling selected sets of genes. However, microorganisms are complex and predicting the effects of gene modification is difficult. Several algorithms have been proposed to support a range of gene knockout strategies, including BAFBA, BHFBA and DBFBA. In this paper, scaling these algorithms and methods to utilise High Performance Computing (HPC) resources have been explored. The applications have been parallelized on HPC and the scalability and performance of these approaches were explored and documented.

关键词： High performance computing HPC Gene knockout Genetic engineering bees algorithm Flux balance

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Data Missing Solution Using Rough Set Theory and Swarm Intelligence

Data Missing Solution Using Rough Set Theory and Swarm Intel...

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International Conference on Advanced Computer Science Applications and Technologies (ACSAT)

作者： Sadiq, Ahmed Tariq Duaimi, Mehdi Gzar Shaker, Samir Adil Univ Technol Baghdad Dept Comp Sci Baghdad Iraq Univ Baghdad Dept Comp Sci Baghdad Iraq

ISBN: (纸本)9780769549590;9781467358323

This paper presents a hybrid approach for solving null values problem;it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estimation through working with rough set theory. The results obtained from most code sets show that bees algorithm better than ID3 in decreasing the number of extracted rules without affecting the accuracy and increasing the accuracy ratio of null values estimation, especially when the number of null values is increasing.

关键词： COMPONENT NULL VALUES PROBLEM ROUGH SET bees algorithm ID3 INCOMPLETE DATABASES

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Modeling of synchronous weapon target assignment problem for howitzer based defense line

Modeling of synchronous weapon target assignment problem for...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Altinoz, O. Tolga Ankara Univ Dept Elect & Elect Engn Ankara Turkey

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

Weapon-target assignment (WTA) is a combinatorial optimization problem in the NP-hard category. The problem is to assign targets to weapons in order to ensure that the targets are eliminated with weapons in the most effective way. This problem, which can be discussed on different scenarios, is called static or dynamic according to the definition of the problem. In this study, the target value and hit probability values will be assigned according to the position of the target and the weapon. The WTA problem will be analyzed in different cases and solved using optimization algorithms. Howitzer will be chosen as weapon and four different types of targets will be offered to get solutions through different scenarios. Three different WTA problems will be examined. The first two scenarios will be solved with single-objective optimization algorithms, and the last case will be modeled as a multi-objective optimization problem.

关键词： weapon-target assignment genetic algorithm particle swarm optimization differential evolution artificial bee colony bees algorithm firefly algorithm simulated annealing invasive weed optimization NSGA-II MOEAD MOPSO

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SVM Parameter Optimization Using Swarm Intelligence for Learning from Big Data 10th

SVM Parameter Optimization Using Swarm Intelligence for Lear...

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10th International Conference on Computational Collective Intelligence (ICCCI)

作者： Xie, Yongquan Murphey, Yi Lu Kochhar, Dev S. Univ Michigan Dearborn MI 48128 USA Ford Motor Co Dearborn MI 48126 USA

ISBN: (纸本)9783319984438;9783319984421

Support vector machine (SVM) is one of the most successful machine learning algorithms to solve practical pattern classification problems. The selection of the kernel function and its parameter plays a vital role on the results. Radius basis function (RBF) is a prevalently used kernel. For an RBF-SVM, two parameters, c and gamma, control the SVM performance. In this paper, we present a SVM parameter learning algorithm, DL&BA, effective for learning from big data. The DL&BA algorithm has two stages. At the first stage, we use a distributed learning (DL) to search for a region which promises optimal parameter pairs. At the second stage, a swarm intelligent optimization algorithm - the bees algorithm (BA) is used to search for an optimal pair of c and.. We applied the DL&BA algorithm to solving an important automotive safety problem, driver fatigue detection, which involves a large amount of real-world driving data. Our experimental results show that DL&BA is not only computational efficient but also effective in finding an optimal pair of c and gamma.

关键词： Support vector machine bees algorithm Fatigue detection

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