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Impact of Levy flight on modern meta-heuristic optimizers

APPLIED SOFT COMPUTING 2019年 75卷 775-789页

作者： Emary, E. Zawbaa, Hossam M. Sharawi, Marwa Cairo Univ Fac Comp & Informat Giza Egypt Arab Open Univ Fac Comp Studies Cairo Egypt Beni Suef Univ Fac Comp & Informat Bani Suwayf Egypt Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania

In this paper, a variant based on Levy flight was proposed to enhance the performance of two recently proposed optimizers. The first optimizer used in the study is Sine-Cosine algorithm ( SCA) while the second is whale optimization algorithm (WOA). Both optimizers are composed of two phases of random walks in each optimization iteration and both have stagnation and premature convergence problems. Levy flight is used to replace the walk based on cosine function in the SCA and the spiral motion in the WOA as well. The Levy-based search guarantees a fraction of solutions to be generated apart from the current best solution and hence tolerates for optimizer stagnation, premature convergence, and allows for local optima avoidance. A smooth control of the scale of the Levy random walk is also proposed to ensure a smooth adaptation of exploration to exploitation switching. The proposed variants, as well as the original algorithms, were benchmarked using a set of unimodal, multimodal, fixed-dimension multimodal and composite benchmark functions. The evaluation is performed using a set of assessment indicators and results prove the capability of the proposed variants to outperform the original optimizers. (C) 2018 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Sine-cosine algorithm Levy flight Levy whale optimization algorithm Levy sine-cosine algorithm

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Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

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JOURNAL OF SUPERCOMPUTING 2019年第10期75卷 6386-6450页

作者： Hemasian-Etefagh, Farinaz Safi-Esfahani, Faramarz Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

Scheduling in cloud computing is the assignment of tasks to resources with maximum performance, which is a multi-purpose problem. The scheduling is of NP-Hard issues that is the reason why meta-heuristic algorithms are used in scheduling problems. The meta-heuristic scheduling algorithms are divided into two categories of biological and non-biological. Swarm-based meta-heuristics are of biological algorithms that are based on imitation, or based on sign. The whale optimization algorithm is a meta-heuristic biological swarm-based intelligence algorithm based on imitation. This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the whale optimization algorithm is introduced that presents a new idea in grouping whales called GWOA. It is firstly proposed to overcome the early convergence problem and then make a balance between the local and the global search in finding the optimal solution. The proposed method divides the sorted population into d groups and a member of each group is randomly selected which is used in encircling prey section of the whale optimization algorithm. Then, the average best fitness was enhanced to improve both exploitation and exploration as well as premature convergence. In the next step, GWOA is used in a cloud computing scheduler at high workload to reduce the average execution time, response time, and increase the throughput in the cloud computing environment. The proposed whale optimization algorithm is compared with the standard whale optimization algorithm (WOA), improved whale optimization algorithm (CWOA), particle swarm optimization (PSO), and bat algorithms applying CEC2017 functions to compare the average parameter of the best fit, and then they are implemented as a cloud computing scheduler. The results of the

关键词： Cloud computing Task scheduling Meta-heuristic algorithm whale optimization algorithm

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Renewable energy source-based multiarea AGC system with integration of EV utilizing cascade controller considering time delay

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第1期29卷 e2646.1-e2646.22页

作者： Saha, Arindita Saikia, Lalit Chandra Natl Inst Technol Silchar Dept Elect Engn Silchar India

Automatic generation control of the unequal 3-area system with diversified sources like small hydrothermal units in area-1 and area-2 and split shaft gas turbine thermal units in area-3 with nonlinearity of generation rate constraint is explored. A cascade combination of integer order integral-derivative with filter (IDN) and fractional order proportional derivative (FOPD) is considered as a secondary controller (IDN-FOPD). The controller gains and other parameters are optimized using whale optimization algorithm. The performance of IDN-FOPD controller has an upper hand when compared with some classical controllers like integral, proportional-integral, and proportional-integral-derivative with filter (PIDN). Analyses reveal that the performance of a combination of IDN-FOPD in area-1 and PIDN controllers in other areas of system dynamics provides comparable results with IDN-FOPD controllers in all areas. Thus, this combination of controllers is carried for further analyses. Even eigenvalue analysis has been performed for analyzing system stability. The impact of time delay on system dynamics is also studied. The impact of the inclusion of renewable source like a solar thermal power plant in area-1 and electric vehicles in all areas provides better system dynamics in the presence of nonlinearity generation rate constraint and time delay. Sensitivity analysis reveals that controller gains and other parameters obtained at nominal conditions are robust to changes in variable solar insolation and demands no further reset.

关键词： electric vehicle generation rate constraint load frequency control solar thermal power plant whale optimization algorithm

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Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2019年第1期22卷 55-66页

作者： Dey, Bishwajit Roy, Shyamal Krishna Bhattacharyya, Biplab IIT ISM Dept Elect Engn Dhanbad Bihar India

The concept of a microgrid system, when put in simple words, is a small scale generation and deployment of power to a small geographical area in order to avoid transmission losses and maintain an uninterrupted power supply. It has been a mandatory protocol to implement the available renewable energy sources (RES) in order to minimize the emission of harmful pollutants to the atmosphere from the combustion of the fossil fuels. Economic load dispatch (ELD) deals with the optimal sizing of the distributed energy resources (DERs) by minimizing the fuel costs. Emission dispatch does the optimal sizing of the DERs sources by minimizing the amount of pollutants released in the atmosphere. A multi-objective Combined Economic-Emission Dispatch (CEED) does the optimal DER sizing providing a compromised solution of minimizing both the fuel costs and pollutants emission. This paper performs all ELD, emission dispatch and CEED on an islanded and renewable-integrated microgrid separately using a recently developed novel whale optimization algorithm (WOA). Four various scenarios of load sharing among the DERs are studied. The results are then compared with other recently developed bio inspired algorithms to corroborate the effectiveness of the proposed technique. Further statistical analysis such as ANOVA test and Wilcoxon signed rank test are performed to prove the superiority of the proposed approach over the various other optimization techniques used. (C) 2018 Karabuk University. Publishing services by Elsevier B.V.

关键词： Combined economic emission dispatch Penalty factor Microgrid Symbiotic organisms search Grey wolf optimization Particle swarm optimization Differential evolution whale optimization algorithm

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A Hybrid Forecasting Model for Short-Term Power Load Based on Sample Entropy, Two-Phase Decomposition and whale algorithm Optimized Support Vector Regression

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IEEE ACCESS 2020年 8卷 166907-166921页

作者： Li, Wenwu Shi, Qiang Sibtain, Muhammad Li, Dan Mbanze, Daniel Eliote China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China China Three Gorges Univ Hubei Key Lab Cascaded Hydropower Stat Operat & C Yichang 443002 Peoples R China Higher Educ Polytech Inst Elect Engn Dept Tete 2304 Mozambique

To improve the accuracy and reliability of short-term power load forecasting and reduce the difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN-SE-VMD-PSR-WOA-SVR) is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to generate multiple intrinsic modal functions (IMF) by decomposing the historical power load series. Then the sample entropy (SE) of each IMF is calculated to quantitatively evaluate the corresponding complexity. Afterward, variational mode decomposition (VMD) is adopted to achieve secondary decomposition for the component with the maximum sample entropy. Subsequently, the phase space reconstruction (PSR) is applied to reconstruct each IMF into a high-dimensional feature space matrix, which is formed as the input of support vector regression (SVR). Finally, SVR optimized by whale optimization algorithm (WOA) is used for the prediction, where the predicted values of all IMFs are accumulated to obtain the final prediction results. The experimental result demonstrates that the proposed hybrid model can effectively decompose the load series with non-linear characteristic and provide more accurate forecasting results by comparing the other models.

关键词： Load modeling Predictive models Load forecasting Prediction algorithms Mathematical model Analytical models Forecasting Short-term power load forecasting two-phase decomposition sample entropy whale optimization algorithm support vector regression

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Optimal renewable resources placement in distribution networks by combined power loss index and whale optimization algorithms

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Journal of Electrical Systems and Information Technology 2018年第2期5卷 175-191页

作者： Dinakara Prasad Reddy P. Veera Reddy V.C. Gowri Manohar T. Department of EEE SV University Tirupati India AITS Tirupati India

Distributed generator (DG) resources are the emerging micro-generating technologies such as fuel cells, micro turbines, IC engines. They also make use of renewable energy sources such as PV arrays and wind turbines. DG units have low emission rates and are environment friendly and economical. Power loss reductions, voltage profile improvement and increasing reliability are some advantages of DG units. The above benefits can be achieved by optimal placement of DGs. Optimal DG locations are obtained from power loss index method. A novel meta heuristic algorithm called whale optimization algorithm (WOA) is used to determine the optimal DG-unit's size in this paper. WOA modeled based on the unique hunting behavior of humpback whales. The WOA algorithm is tested on IEEE 15-bus, 33-bus, 69-bus, 85-bus and 118-bus test systems. The results obtained by the proposed WOA algorithm was compared with different types of DGs and other evolutionary algorithms. When compared with other algorithms the WOA algorithm gives better results. From the analysis best results have been achieved from type III DG operating at 0.9 pf.

关键词： whale optimization algorithm Power loss index method Distributed generation placement Radial distribution system Loss reduction

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End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA

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TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS 2019年第1期72卷 257-270页

作者： Gao, Chuang Shen, Minggang Liu, Xiaoping Wang, Lidong Chen, Ming Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China Univ Sci & Technol Liaoning Sch Met & Mat Anshan Liaoning Peoples R China Lakehead Univ Fac Engn Thunder Bay ON P7B 5E1 Canada

Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Therefore, research on BOF steelmaking modeling is very necessary. In this paper, a novel combination prediction model has been proposed, which consists of a time series prediction model and a compensation prediction model. Both models are established by k-nearest neighbor-based weighted twin support vector regression (KNNWTSVR) algorithm. By introducing Levy flight algorithm and inertia weight, an improved algorithm of whale optimization algorithm (WOA) called Levy flight WOA has been initially proposed to solve the optimization problem in the objective function of KNNWTSVR. The simulation results show that the proposed models are effective and feasible. Within different error bounds (0.005% for carbon content model and 10 degrees C for temperature model), the strike rates of carbon content and temperature both achieve 93%, and a double strike rate of 86% is obtained, which can provide a significant reference for real BOF applications, and the proposed method is also appropriate for the prediction models of other metallurgical applications.

关键词： Basic oxygen furnace Twin support vector regression whale optimization algorithm Prediction model

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Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 168卷 1页

作者： Wu, Lifeng Huang, Guomin Fan, Junliang Ma, Xin Zhou, Hanmi Zeng, Wenzhi Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Northwest A&F Univ Minist Educ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Yangling 712100 Shaanxi Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Henan Univ Sci & Technol Coll Agr Engn Luoyang 471003 Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China

Accurate estimation of pan evaporation (E-p) is of great significance to the development of agricultural irrigation systems and agricultural water resources management. The purpose of this study was to explore the applicability of coupling extreme learning machine (ELM) model with two new meta-heuristic algorithms, i.e. whale optimization algorithm (WOA) and flower pollination algorithm (FPA) for monthly E-p prediction. To achieve this goal, two hybrid models of WOAELM and FPAELM were developed for predicting monthly E-p in the Poyang Lake Basin of Southern China as a case study. Their performances were further compared with the differential evolution algorithm-optimized ELM (DEELM), improved M5 model tree (M5P) and artificial neural networks (ANN) models. Monthly climatic parameters, including maximum and minimum temperature (T-max and T-min), sunshine duration (n), relative humidity (RH), wind speed (U) and E-p from four weather stations in the basin from 2001 to 2015 were collected, those of which during 2001-2010 were used for model training and those during 2011-2015 for model testing. The obtained results showed that the hybrid FPAELM model exhibited the highest prediction accuracy at all the four stations, followed by the hybrid WOAELM model, both of which were superior to the other traditional models. Heuristic algorithms, especially FPA, are highly recommended for improving performance of standalone machine learning models. Compared with the combination of multi-meteorological elements, the combination of Tmax Tmin and extraterrestrial solar radiation achieved higher but still satisfactory prediction accuracy, with the absolute error less than 0.1 mm d(-1) averaged over the four stations. Tmax Tmin and extraterrestrial solar radiation were thus suggested to be used for monthly E-p estimation in this area considering the convenience of data acquisition.

关键词： Pan evaporation Extreme learning machine whale optimization algorithm Flower pollination algorithm

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Joint Task Offloading and Resource Management in NOMA-Based MEC Systems: A Swarm Intelligence Approach

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IEEE ACCESS 2020年 8卷 190463-190474页

作者： Pham, Huong-Giang T. Quoc-Viet Pham Pham, Anh T. Nguyen, Chuyen T. Hanoi Univ Sci & Technol Sch Elect & Telecommun Hanoi 100000 Vietnam Pusan Natl Univ Res Inst Comp Informat & Commun Busan 46241 South Korea Univ Aizu Comp Commun Lab Aizu Wakamatsu Fukushima 9658580 Japan

In this paper, we study the issue of computation offloading in non-orthogonal multiple access (NOMA)-based multi-access edge computing (MEC) systems. A joint optimization problem of offloading decision, subchannel assignment, transmit power, and computing resource allocation is investigated to improve system performance in terms of both completion time and energy consumption. The formulated problem is a mixed-integer non-linear programming one, it is therefore hard to solve. To make the problem tractable, we first decompose the problem into subproblems of computing resource allocation (CRA), transmit power control (TPC), and subchannel assignment (SA). Then, we address the CRA subproblem by a convex optimization technique. For the remaining two subproblems TPC and SA, we propose to use a gradient-free swarm intelligence approach, namely whale optimization algorithm, to provide a very general but efficient solution. Computer simulations are performed to show the convergence of the proposed algorithm, also its better performance in comparison with conventional schemes.

关键词： Task analysis optimization Particle swarm optimization NOMA Servers Resource management Energy consumption Computation offloading swarm intelligence multi-access edge computing non-orthogonal multiple access resource management whale optimization algorithm

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Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis

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APPLIED SOFT COMPUTING 2020年第0期88卷 105946-000页

作者： Wang, Mingjing Chen, Huiling Hangzhou Med Coll Hangzhou 310053 Zhejiang Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to perform parameter optimization and feature selection simultaneously for SVM, we propose an improved whale optimization algorithm (CMWOA), which combines chaotic and multi-swarm strategies. Using several well-known medical diagnosis problems of breast cancer, diabetes, and erythemato-squamous, the proposed SVM model, termed CMWOAFS-SVM, was compared with multiple competitive SVM models based on other optimization algorithms including the original algorithm, particle swarm optimization, bacterial foraging optimization, and genetic algorithms. The experimental results demonstrate that CMWOAFS-SVM significantly outperformed all the other competitors in terms of classification performance and feature subset size. (C) 2019 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Multi-swarm Support vector machine Model selection Medical diagnosis

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