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A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 73卷 111-125页

作者： Abualigah, Laith Mohammad Khader, Ahamad Tajudin Hanandeh, Essam Said Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zarqa Univ Dept Comp Informat Syst POB 13132 Zarqa Jordan

krill herd (KH) algorithm is a novel swarm-based optimization algorithm that imitates krill herding behavior during the searching for foods. It has been successfully used in solving many complex optimization problems. The potency of this algorithm is very high because of its superior performance compared with other optimization algorithms. Hence, the applicability of this algorithm for text document clustering is investigated in this work. Text document clustering refers to the method of clustering an enormous amount of text documents into coherent and dense clusters, where documents in the same cluster are similar. In this paper, a combination of objective functions and hybrid KH algorithm, called, MHKHA, is proposed to solve the text document clustering problem. In this version, the initial solutions of the KH algorithm are inherited from the k-mean clustering algorithm and the clustering decision is based on two combined objective functions. Nine text standard datasets collected from the Laboratory of Computational Intelligence are used to evaluate the performance of the proposed algorithms. Five evaluation measures are employed, namely, accuracy, precision, recall, F-measure, and convergence behavior. The proposed versions of the KH algorithm are compared with other well-known clustering algorithms and other thirteen published algorithms in the literature. The MHKHA obtained the best results for all evaluation measures and datasets used among all the clustering algorithms tested.

关键词： Combination of objective functions Hybridization krill herd algorithm K-mean algorithm Text document clustering

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Model turbine heat rate by fast learning network with tuning based on ameliorated krill herd algorithm

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KNOWLEDGE-BASED SYSTEMS 2017年 118卷 80-92页

作者： Niu, Peifeng Chen, Ke Ma, Yunpeng Li, Xia Liu, Aling Li, Guoqiang Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Peoples R China Yuanshang Cent Middle Sch Laixi 266600 Shandong Peoples R China

The krill herd (KH) is an innovative biologically-inspired algorithm. To improve the solution quality and to quicken the global convergence speed of KH, an ameliorated krill herd algorithm (A-KH) is proposed to solve the aforementioned problems and test it by classical benchmark functions, which is one of the major contributions of this paper. Compared with other several state-of-art optimization algorithms (biogeography-based optimization, particle swarm optimization, artificial bee colony and krill herd algorithm), A-KH shows better search performance. There is, furthermore, another contribution that the A-KH is adopted to adjust the parameters of the fast learning network (FLN) so as to build the turbine heat rate model of a 600MW supercritical steam and obtain a high-precision prediction model. Experimental results show that, compared with other several turbine heat rate models, the tuned FLN model by A-KH has better regression precision and generalization capability. (C) 2016 Elsevier B.V. All rights reserved.

关键词： krill herd algorithm Fast learning network Heat rate Steam turbine

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Novel hybrid modified krill herd algorithm and fuzzy controller based MPPT to optimally tune the member functions for PV system in the three-phase grid-connected mode

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ISA TRANSACTIONS 2022年第PT B期129卷 214-229页

作者： Hu, Zhuxin Norouzi, Hojat Jiang, Mingxin Dadfar, Sajjad Kashiwagi, Tetsuya Wenzhou Univ Coll Educ Wenzhou 325035 Zhejiang Peoples R China Solar Energy & Power Elect Co Ltd Tokyo Japan Huaiyin Inst Technol Fac Elect informat Engn Huaian 223003 Peoples R China Islamic Azad Univ Dept Elect Engn Saveh Branch Saveh Iran

For the technical, economic and environmental benefits, using solar energy has increased worldwide, especially with the help of photovoltaic (PV) systems. The amount of energy produced in PV systems is dependent on environmental circumstances such as temperature and solar irradiance. In order to extract the maximum possible power in PV systems and increase the efficiency under various environmental conditions, maximum power point tracking (MPPT) controllers have been proposed. To fine-tune the control parameters of the proposed MPPT approach, the fuzzy controller and modified krill herd (MKH) algorithm are jointly employed. Rule base and membership functions (MFs) are two important parameters for implementing the FLC and need to be fine-tune appropriately. However, in the condition where precise information concerning the system is not available, the fine-tuning of these parameters cannot be accurate. To cope with this problem, the MKH algorithm is used to optimize the scaling factors of MFs. To improve the stability of the system under study, the PV system is used with the storage system at the same time. This hybrid system can deal with the stochastic nature of the PV system and provide more stability in all atmospheric conditions. The proposed MPPT method is confirmed by comparing it with other well-known techniques.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： MPPT Photovoltaic (PV) Battery Fuzzy logic control krill herd algorithm

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A novel hybridization strategy for krill herd algorithm applied to clustering techniques

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APPLIED SOFT COMPUTING 2017年 60卷 423-435页

作者： Abualigah, Laith Mohammad Khader, Ahamad Tajudin Hanandeh, Essam Said Gandomi, Amir H. Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zarqa Univ Dept Comp Informat Syst POB 13132 Zarqa Jordan Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： krill herd algorithm Hybridization Global exploration Data clustering Text clustering

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Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第16期33卷 10005-10020页

作者： Aldosary, Abdallah Rawa, Muhyaddin Ali, Ziad M. Latifi, Mohsen Razmjoo, Armin Rezvani, Alireza Prince Sattam Bin Abdulaziz Univ Dept Comp Sci As Sulayyil 11991 Saudi Arabia King Abdulaziz Univ Fac Engn Ctr Res Excellence Renewable Energy & Power Syst KA CARE Energy Res & Innovat Ctr Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn KA CARE Energy Res & Innovat Ctr Jeddah 21589 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt Islamic Azad Univ Dept Mech Engn Sci & Res Branch Tehran Iran Univ Politecn Catalunya UPC Escola Tecn Super Engn Ind Barcelona ETSEIB Barcelona 08028 Spain Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam

Providing of energy is one of the most important issues for each country. Also, environmental issues due to fossil fuel depletion are other serious concern of them. In this regard, moving toward energy sustainability is a constructive solution for each country. This paper studies the short-term planning of generating units in renewable energy-based distribution networks equipped with plug-in electric vehicles (PEVs). PEVs can cause problems for distributed energy sources in the electrical grid, as well as power units inside the grid. So, to overcome this problem, an efficient stochastic programming technique is designed to allow the control entity to control the charging behavior of PEVs for managing power units. In this paper, to obtain the least total cost, a new method is suggested to decrease the reliability expenses. In other words, the vehicle-2-grid (V2G) is applied to decrease the operating. On the other hand, a novel stochastic flow using the unscented transform is suggested to improve the model of the severe uncertainty due to the wind power, photovoltaic (PV) and charging/discharging power of PEVs. In this research work, a novel and efficient optimization algorithm called 'theta-modified krill herd (theta-MKH)" is used as an applicable technique to optimize the microgrid (MG) operation. This algorithm is useful and has many advantages like the runaway from the local optima with fast converging in comparison with other methods. Also, the satisfactory efficiency of the suggested randomized manner is validated on an MG connected to the main grid.

关键词： Stochastic management Renewable energy Microgrid (MG) krill herd algorithm Electric vehicle

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Training Neural Networks with krill herd algorithm

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NEURAL PROCESSING LETTERS 2016年第1期44卷 5-17页

作者： Kowalski, Piotr A. Lukasik, Szymon Polish Acad Sci Syst Res Inst Ul Newelska 6 PL-01447 Warsaw Poland AGH Univ Sci & Technol Fac Phys & Appl Comp Sci Al A Mickiewicza 30 PL-30059 Krakow Poland

In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that of the krill herd algorithm (KHA) procedure. It contains many interesting mechanisms. The purpose of this article is to compare the KHA optimization algorithm used for learning an artificial neural network (ANN), with other heuristic methods and with more conventional procedures. The proposed ANN training method has been verified for the classification task. For that purpose benchmark examples drawn from the UCI Machine Learning Repository were employed with Classification Error and Sum of Square Errors being used as evaluation criteria. It has been concluded that the application of KHA offers promising performance-both in terms of aforementioned metrics, as well as time needed for ANN training.

关键词： krill herd algorithm Biologically Inspired algorithm Metaheuristic Neural Networks Optimization

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Sizing and Shape Optimization of Discrete Truss Employing a Target-Oriented krill herd algorithm

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING 2024年第2期10卷 021108页

作者： Cheng, Lixiang Zhao, Yan-Gang Li, Pei-Pei Yan, Lewei Kanagawa Univ Dept Architecture Yokohama 2218686 Japan Beijing Univ Technol Sch Civil Engn Beijing 100124 Peoples R China TU Dortmund Univ Chair Reliabil Engn D-44227 Dortmund Germany Guangzhou Univ Coll Civil Engn Guangzhou 510006 Peoples R China

The krill herd (KH) algorithm is widely used for optimizing truss structures as no gradient information is necessary, and only a few parameters require adjustment. However, when the truss structure becomes discrete and complex, KH tends to fall into a local optimum. Therefore, a novel target-oriented KH (TOKH) algorithm is proposed in this study to optimize the design of discrete truss structures. Initially, a crossover operator is established between the "best krill" and "suboptimal krill" to generate a robust "cross krill" for global exploration. Additionally, an improved local mutation and crossover (ILMC) operator is introduced to fine-tune the "center of food" and candidate solutions for local exploitation. The proposed method and other optimization approaches are experimentally compared considering 15 benchmark functions. Then, the performance of the TOKH algorithm is evaluated based on four discrete truss structure optimization problems under multiple loading conditions. The obtained optimization results indicate that the proposed method presents competitive solutions in terms of accuracy, unlike other algorithms in the literature, and avoids falling into a local minimum. TOKH algorithm flowchart

关键词： discrete truss optimization krill herd algorithm target-oriented robustness

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A Chaotic krill herd algorithm for Optimal Solution of the Economic Dispatch Problem

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INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH IN AFRICA 2017年 31卷 155-168页

作者： Bentouati, Bachir Chettih, Saliha El-Sehiemy, Ragab A. Amar Telidji Univ Laghouat Dept Elect Engn LACoSERE Lab Laghouat Algeria Kafrelsheikh Univ Fac Engn Dept Elect Engn Kafr El Sheikh Algeria Kafrelsheikh Univ ISRG Kafr El Sheikh Algeria

The aim of economic dispatch (ED) problem is to provide an efficient utilization of energy resources to produce economic and secure operating conditions for the planning and operation of a power system. ED is formed as a nonlinear optimization problem with conflicting objectives and subjected to both inequality and equality constraints. An efficient improvement of krill herd (KH) algorithm, a powerful metaheuristic method, has been introduced in this paper. The KH algorithm inspired by the Lagrangian and evolutionary behaviour of the krill people in nature, has been investigated to solve ED problem on 6, 13, 20 and 40 generating units. The proposed chaotic krill herd (CKH)) improvement is done by incorporating the chaos approach to KH algorithm for raising the global convergence speed and for enhancing its performance. The elitism scheme serves to save the best krill during the procedure when updating the krill. The results show clearly the superiority of CKH in searching for the best cost value results when compared with well-known metaheuristic search algorithms.

关键词： krill herd algorithm Chaotic maps Economic dispatch Valve-point effect

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Hybrid krill herd algorithm with Vortex Search for Global Numerical Optimization

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Wuhan University Journal of Natural Sciences 2020年第2期25卷 109-117页

作者： YANG Jian WAN Zhongping PENG Zhenhua School of Mathematics and Statistics Wuhan UniversityWuhan 430072HubeiChina

In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimization *** improvement is that a new hybrid vortex search(HVS)operator is added into the updating process of the krill for the purpose of dealing with optimization problems more *** 20 benchmark functions for comparison experiments,the results show that the VSKH algorithm has higher accuracy.

关键词： krill herd algorithm global optimization problem hybrid vortex search operator(HVS)

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krill herd algorithm for optimum design of truss structures

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2013年第5期5卷 281-288页

作者： Gandomi, Amir Hossein Talatahari, Siamak Tadbiri, Faraz Alavi, Amir Hossein Univ Akron Dept Civil Engn Akron OH 44325 USA Univ Tabriz Marand Fac Engn Tabriz *** Iran Islamic Azad Univ Shabestar Branch Dept Struct Engn Shabestar 538165 Iran Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

krill herd (KH) algorithm, as a new metaheuristic optimisation, is developed to solve truss optimisation problems. The KH methodology draws its analogy from the herding behaviour of krill individuals in nature. The objective function for the krill movement is mostly influenced by the least distances of each krill individual from food and from highest density of the herd. The new algorithm is examined by solving three truss design optimisation problems and its performance is further compared with various classical and advanced algorithms.

关键词： metaheuristic optimum design truss structures krill herd algorithm

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