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Solution of optimal power flow using chaotic krill herd algorithm

CHAOS SOLITONS & FRACTALS 2015年 78卷 16-27页

作者： Mukherjee, Aparajita Mukherjee, V. Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India

Recently, krill herd algorithm (KHA) is proposed for the solution of global optimization problem. Various chaotic maps are considered in the proposed chaotic KHA (CKHA) of the present work to improve the performance of basic KHA method. It is observed that Logistic map based CKHA offers better results as compared other chaotic maps. The performance of the proposed CKHA is tested on standard 26-bus and IEEE 57-bus test power systems for the solution of optimal power flow of power system with different objectives that reflect minimization of fuel cost or active power loss or sum of total voltage deviation. Results obtained by using CKHA are compared to other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. The results presented in this paper show that the proposed CKHA algorithm outperforms the other techniques in terms of convergence rate and global search ability. (C) 2015 Elsevier Ltd. All rights reserved

关键词： Chaos krill herd algorithm Optimal power flow Optimization

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A parallel hybrid krill herd algorithm for feature selection

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第3期12卷 783-806页

作者： Abualigah, Laith Alsalibi, Bisan Shehab, Mohammad Alshinwan, Mohammad Khasawneh, Ahmad M. Alabool, Hamzeh Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town Malaysia Aqaba Univ Technol Comp Sci Dept Aqaba Jordan Saudi Elect Univ Coll Comp & Informat Abha Saudi Arabia

In this paper, a novel feature selection method is introduced to tackle the problem of high-dimensional features in the text clustering application. Text clustering is a prevailing direction in big text mining;in this manner, documents are grouped into cohesive groups by using neatly selected informative features. Swarm-based optimization techniques have been widely used to select the relevant text features and shown promising results on multi-sized datasets. The performance of traditional optimization algorithms tends to fail miserably when using large-scale datasets. A novel parallel membrane-inspired framework is proposed to enhance the performance of the krill herd algorithm combined with the swap mutation strategy (MHKHA). In which the krill herd algorithm is hybridized the swap mutation strategy and incorporated within the parallel membrane framework. Finally, the k-means technique is employed based on the results of feature selection-based krill herd algorithm to cluster the documents. Seven benchmark datasets of various characterizations are used. The results revealed that the proposed MHKHA produced superior results compared to other optimization methods. This paper presents an alternative method for the text mining community through cohesive and informative features.

关键词： Feature selection Document clustering Parallel membrane computing krill herd algorithm Local search Optimization problem

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Application of chaotic krill herd algorithm for optimal power flow with direct current link placement problem

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CHAOS SOLITONS & FRACTALS 2017年 103卷 90-100页

作者： Prasad, Dharmbir Mukherjee, Aparajita Mukherjee, V. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Indian Sch Mines Dept Elect Engn Indian Inst Technol Dhanbad Jharkhand India

In this paper, a real parameter metaheuristic optimization algorithm (named as chaotic krill herd algorithm (KHA) (CKHA)) is analyzed to solve optimal power flow (OPF) based DC link placement problem. The present study appears to be well capable for replacement of the existing transmission lines by direct current (DC) links to have more secured, flexible and economical operation. DC link equations are incorporated into the conventional OPF problem for the solution of this type of problem. The IEEE 30-bus and IEEE 57-bus test power systems are used to demonstrate the performance of the proposed CKHA. The simulation results obtained from both KHA and CKHA techniques are compared to other recent evolutionary optimization techniques surfaced in the recent state-of-the-art literature. It is revealed that the proposed approach secures better consequence over the other newly originated popular optimization techniques (including basic KHA) and reflects its improved quality solutions and faster convergence speed. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： AC/DC load flow Chaos krill herd algorithm Optimal power flow Optimization

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Economic load dispatch using krill herd algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第May期57卷 1-10页

作者： Mandal, Barun Roy, Provas Kumar Mandal, Sanjoy Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India

Economic load dispatch (ELD) is an important topic in the operation of power plants which can help to build up effective generating management plans. The practical ELD problem has non-smooth cost function with nonlinear constraints which make it difficult to be effectively solved. This paper presents, a new and efficient krill herd algorithm (KHA) to solve both convex and non-convex ELD problems of thermal power units considering valve point loading, multiple fuel operation, transmission losses and constraints such as ramp rate limits and prohibited operating zones. To enhance the overall performance and effectiveness of the proposed algorithm, the crossover and mutation operation of differential evolution (DE) are integrated with the proposed method. The different versions of KHA are successfully applied to small, medium, and large-scale power systems for solving six different ELD problems. The simulation results obtained by the proposed algorithms are compared with the results obtained using other recently develop methods available in the literature. From numerical results, it is found that the proposed KHA with crossover and mutation operators approach is able to provide better solution than other reported techniques in terms of fuel cost. Furthermore, this algorithm is better in terms of robustness than most of the existing algorithms used in this study. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Economic load dispatch Valve point loading Prohibited zone krill herd algorithm Differential evolution

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

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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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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PartB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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