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International Congress on Technology, Communication and Knowledge (ICTCK)

作者： Lari, Nazanin Sadeghi Abadeh, Mohammad Saniee Islamic Azad Univ Dept Elect Comp & IT Engn Qazvin Branch Qazvin Iran Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781479980215

Data classification is an important branch of data mining and there are different methods for its implementation. Neural networks are one of the best ways for classification in machine learning. Structure and weights of neural network are most important in their precision. In recent years, due to the defects in gradient-based search algorithms in neural network training algorithms, metahuristic algorithms have been of interest for researchers. Due to the random nature of these algorithms, the defects of trapped in local minimum can be largely resolved but Since training the weights of the neural network was done on specific network architecture, there were no guarantees for selecting the best architecture. So, in our work, krill herd algorithm was used to improve the structure addition of network weights. Task of optimizing the network structure was on the three components of this algorithm (movement induced by the other krill, random diffusion, and foraging motion) along with a genetic operator;also dimensions of krill showed the desired structure for the neural network. In this paper, the performance of the proposed method was tested on five UCI data sets and the results compared with the previous methods showed that the classification accuracy of the proposed method was considerably higher and the mean square error was low.

关键词： krill herd algorithm neural networks classification optimization metahuristic

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A robust MPC for grid-following inverters improved with the krill herd optimization algorithm

A robust MPC for grid-following inverters improved with the ...

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Power Electronics and Control (SEPOC), Seminar on

作者： Paulo Jefferson Dias de Oliveira Evald José E. Das Neves Da Fonseca Matheus Schramm Dall’Asta Jéssika Melo de Andrade Lenon Schmitz Telles Brunelli Lazzarin Federal University of Santa Catarina Florianópolis Brazil Federal University of Pelotas Pelotas Brazil Intelligent Systems and Control Group Federal University of Pelotas Pelotas Brazil Institute of Power Electronics Federal University of Santa Catarina Florianópolis Brazil

ISBN: (数字)9798350379419

ISBN: (纸本)9798350379426

LCL filters are commonly used to interface the inverters and the electrical grid on grid-tied systems. However, its control is a challenge due to the resonance peak. Conversely, its model is well-known. Taking this into account, this work presents a model predictive controller with a robust adaptive disturbance rejection to control the grid-injected current of a voltage source inverter with an LCL filter. In addition, the disturbance rejection controller is tuned with the krill herd optimization algorithm. An in-depth discussion about optimizer configuration is provided to assist controller designers. The results demonstrated that system response has a short transient regime, around a quarter grid cycle, even when grid impedance changes.

关键词： Seminars Adaptation models Renewable energy sources Filters Voltage source inverters Grid following krill herd algorithm Resonance Closed loop systems Transient analysis

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Application of krill herd(KH) algorithm for Production Scheduling in Capital Goods Industries

Application of Krill Herd(KH) Algorithm for Production Sched...

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International Conference on challenges in IT, Engineering and Technology (ICCIET’2014, Phuket)

作者： Hathaithanok Puongyeam Pupong Pongcharoen Srisatja Vitayasak Centre for Operation Research and Industrial Applications (CORIA) Faculty of Engineering Naresuan University

Production scheduling is one of the essential business operations particularly in capital goods company preferring to reduce inventory by delivery of goods based on limited time whilst simultaneously accomplish high resource utilisation. The capital goods industry manufactures complex products with highly customised and manufactured in low volume on the make/engineerto-order basis. Effective production scheduling must be met the customer due dates as well as minimised the total earliness and tardiness penalties cost. This paper presents the application of the krill herd(KH) algorithm and proposes the Modified krill herd algorithm(MKH) for solving production scheduling problem. The computational experiments were carried out using various sizes of scheduling problem obtained from a capital goods company. The analysis on the computational results indicates that the MKH algorithm significantly performed better than the conventional KH algorithm for all problems.

关键词： krill herd algorithm scheduling capital goods

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KH-FC: krill herd-based fractional calculus algorithm for text document clustering using MapReduce structure

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2022年第6期25卷 668-684页

作者： More, Priyanka Shivaprasad Saini, Baljit Singh Lovely Profess Univ Sch Comp Sci & Engn Phagwara Punjab India

In this paper, krill herd-based fractional calculus (KH-FC) using MapReduce framework is proposed for effective text document clustering. Here, the stop word removal and stemming model is applied in the pre-processing step, helps to remove redundant information and hence the size of the information is reduced, which further enhances the clustering accuracy. Furthermore, term frequency (TF) and inverse document frequency (IDF) are employed for extracting significant features. Finally, the developed KH-FC model is utilised for clustering the text documents. The developed KH-FC algorithm is developed by combining the FC concept into the KH technique. In this method, pre-processing and feature extraction is performed in the mapper phase, whereas the clustering process is executed in the reducer phase. The performance of the developed approach is evaluated based on performance metrics, like accuracy, Jaccard coefficient, and F-measure. The developed KH-FC approach obtained better performance in terms of accuracy, Jaccard coefficient, and F-measure is 0.983, 0.936 and 0.967, respectively.

关键词： text document clustering fraction calculus krill herd algorithm Jaccard similarity term frequency-inverse document frequency IDF

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Application of improved krill herd algorithms to inverse radiation problems

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2016年 103卷 24-34页

作者： Ren, Ya-Tao Qi, Hong Huang, Xing Wang, Wei Ruan, Li-Ming Tan, He-Ping Harbin Inst Technol Sch Energy Sci & Engn 92 West Dazhi St Harbin 150001 Peoples R China Civil Aviat Univ China Tianjin Peoples R China

A nature-inspired metaheuristic krill herd (KH) algorithm was introduced to inverse transient radiative problems for the first time. Three improved KH (IKH) algorithms were developed to accelerate convergence in the later stage of the searching process. Radiative source terms were then estimated in a parallel slab of participating medium to investigate the performance of the proposed algorithms. Results showed the superiority of the three IKH algorithms to the original one. Additionally, the extinction coefficient and scattering albedo in a parallel slab with short pulse laser incident were retrieved using the IKH algorithms. Radiative properties cannot be estimated accurately with measurement errors when the media were optically thick and scattering dominated in the inverse transient radiative problems. Hence, adopting a shorter incident pulse was suggested to solve this problem. Consequent numerical simulations indicated that radiative properties can be retrieved accurately even with measurement errors. Finally, the proposed IKH algorithms were applied to a 2D media to obtain the optical properties of the inclusion. (C) 2015 Elsevier Masson SAS. All rights reserved.

关键词： Inverse problems Transient radiative transfer krill herd algorithm Radiative properties

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Fuzzy Extended krill herd Optimization with Quantum Bat algorithm for Cluster Based Routing in Mobile Adhoc Networks

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淡江理工学刊 2022年第4期25卷 733-740页

作者： Maganti Srinivas M.Ramesh Patnaik

MANET integrates a set of autonomous mobile nodes which move independently and send data through wireless links. Clustering and routing are the commonly employed energy efficient techniques, which can be treated as an NP hard problem and is resolved by computational intelligence algorithms. The mobility of the nodes leads to repeated link failures and low energy efficiency. In order to achieve high energy efficiency and network connectivity, this paper presents a new Fuzzy Extended krill herd Optimization with Quantum Bat algorithm (FEKHO-QBA) for Cluster Based Routing in MANET. The presented model uses FEKHO algorithm by integrating the concepts of fuzzy logic and KHO algorithm for clustering process and effective selection of cluster heads (CHs). Besides, the QBA is applied as a routing technique to determine the optimal paths to the destination nodes. The QBA involves the features of faster convergence rate, easier to implement, and improved accurateness. The application of FEKHO-QBA algorithm offers maximum energy efficiency and network longevity. For determining the effectual performance of the FEKHO-QBA algorithm, a set of different experiments were carried out and highlighted the supremacy over the compared methods interms of different performance measures.

关键词： MANET Clustering Routing krill herd algorithm Bat algorithm

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Gravitational Search, Monkey, and krill herd Swarm algorithms for Phase Stability, Phase Equilibrium, and Chemical Equilibrium Problems

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CHEMICAL ENGINEERING COMMUNICATIONS 2016年第3期203卷 389-406页

作者： Khalil, Ahmed M. E. Fateen, Seif-Eddeen K. Bonilla-Petriciolet, Adrian Cairo Univ Fac Engn Dept Chem Engn Giza 12211 Egypt Amer Univ Cairo Dept Petr & Energy Engn New Cairo Egypt Inst Tecnol Aguascalientes Dept Chem Engn Aguascalientes Mexico

Phase equilibrium calculations (PECs) and phase stability (PS) analysis of reactive and nonreactive systems problems are important for the simulation and design of chemical engineering processes. These problems, which are challenging, multi-variable, and non-convex, require optimization techniques that are both efficient and effective in finding the solution. Stochastic global optimization algorithms, especially swarm algorithms, are promising tools for such problems. In this study, monkey algorithm (MA), gravitational search algorithm (GSA), and krill herd algorithm (KHA) were used to solve PS, phase equilibrium, and chemical equilibrium problems. We have also studied the effect of adding a local optimizer at the end of the stochastic optimizer run. The results were compared to determine the strengths and weaknesses of each algorithm. When a local optimizer was used, MA was found to be a reliable algorithm in solving the problems. GSA had relatively the least numerical effort for all problems among the three algorithms but with low reliability. KHA was more reliable than other two algorithms without the use of a local optimizer. The performance of GSA, MA, and KHA was compared with firefly algorithm and cuckoo search (CS). In summary, this study found that CS algorithm was more reliable than the newly tested algorithms. Nevertheless, MA and GSA algorithms, when combined with a local optimizer, solve the thermodynamic problems as reliably and efficiently as CS.

关键词： Gravitational search algorithm krill herd algorithm Monkey algorithm Phase and chemical equilibrium calculations Phase stability analysis Stochastic swarm algorithms

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Cuckoo, Bat and krill herd based k-means plus plus clustering algorithms

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 14169-14180页

作者： Aggarwal, Shruti Singh, Paramvir Sri Guru Granth Sahib World Univ Dept CSE Fatehgarh Sahib Punjab India Natl Inst Technol Dept CSE Jalandhar Punjab India

Traditional k-means clustering algorithm is sensitive to the choice of initial cluster centers and leads to local optimal results. k-means++ is a hybrid k-means clustering algorithm which specifies the procedure to initialize the cluster centers before proceeding with the standard k-means algorithm. Inspired by nature, some contemporary optimization techniques such as Cuckoo, Bat and krill herd algorithms etc., are used for optimization as they mimic the swarming behaviour and allows to cooperatively move towards an optimal objective within a reasonable time. In this paper, these nature-inspired techniques are used for optimizing k-means++ clustering algorithm to enhance clustering quality and generate new hybrids of unprecedented performance. The results of the evaluation experiments on the integration of nature-inspired optimization methods with k-means++ algorithm are reported.

关键词： k-means k-means plus Nature inspired algorithms Swarm based techniques Cuckoo search algorithm Bat algorithm krill herd algorithm

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Current studies and applications of krill herd and Gravitational Search algorithms in healthcare

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 1期56卷 1243-1277页

作者： Hamad, Rebwar Khalid Rashid, Tarik A. Erbil Polytech Univ Erbil Tech Engn Coll Dept Informat Syst Engn Erbil Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the krill herd algorithm. The study on using the krill herd algorithm (KH) and the Gravitational Search algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.

关键词： Healthcare Meta-huristic Nature inspired computing krill herd algorithm Gravitational Search algorithm

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Design performance optimization of laser beam welded joints made for vehicle chassis application using deep neural network-based krill herd method

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WELDING INTERNATIONAL 2023年第7期37卷 365-386页

作者： Surwase, Sanjay S. Bhosle, Santosh P. Maharashtra Inst Technol Dept Mech Engn Aurangabad 431010 Maharashtra India Maharashtra Inst Technol Aurangabad India

The welding to be performed must be defect free, offer lower residual stress and strain, be compact in size and withstand different load conditions. However, the existing investigations in this scenario are still not modernized. Therefore, in this study, a specific welding method called laser beam welding (LBW) is performed and different weld parameters have been inspected and analysed. Advanced instruments based on the non-destructive (ND) are implemented to find the variable LBW responses such as weld bead defects, residuals and strain. The experimentation has been designed using Design Expert software, response surface methodology (RSM) and Box Behnken design (BBD) and verified by analysis of variance (ANOVA) analysis and FIT statistics. Moreover, a hybrid deep neural network-based krill herd optimization (DNN-KHO) is implemented to predict the output parameters like, undercut (mu m), overlap (mu m), total strain (mm/mm) and residual stress (MPa) during welding. The proposed DNN-KHO was also used to optimize LBW input parameters such as, peak power (W), weld speed (mm/s), gas flow rate (l/min) and beam diameter (mu m) simultaneously. Predictions show that the proposed DNN-KHO algorithm outperformed by 21.53%, 45.428% and 41.31% higher in accuracy compared to respective hybrid random forest based grey wolf method (RF-GWO), RF and DNN predictions.

关键词： Laser beam welding response surface methodology ANOVA krill herd algorithm deep neural network

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