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whale optimisation algorithm for automatic generation control of interconnected modern power systems including renewable energy sources

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第3期12卷 607-614页

作者： Hasanien, Hany M. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

Till this moment, the model of interconnected power systems in the automatic generation control (AGC) loops relies only on the synchronous generating units. In today's world, a high level of penetration of renewable energy sources (RES) is integrated to the power grids. This paper presents a novel power system model, which includes both conventional generating units and RES for studying the AGC problem of such systems. The control strategy in the AGC loops is based on the proportional-integral-derivative (PID) controller, which is optimally designed by the whale optimization algorithm (WOA). It represents a great challenge to this controller to deal with the RES uncertainties. The effectiveness of the WOA-based PID controller is compared with other computation evolutionary algorithms-based PID controller. The system performance is evaluated under different operating conditions. For achieving a realistic study, 1) real wind speed data that extracted from Zafarana location in Egypt are used, 2) solar irradiation and temperature data that extracted from a field test are implemented, and 3) an irregular wave energy condition is applied. The validity of the control strategy is verified using the simulation results, which are carried out using MATLAB environment.

关键词： power system interconnection evolutionary computation maximum power point trackers three-term control power generation control renewable energy sources whale optimisation algorithm automatic generation control interconnected modern power systems renewable energy sources power system model RES AGC problem control strategy AGC loops proportional-integral-derivative controller PID controller WOA evolutionary algorithms solar irradiation temperature data irregular wave energy condition maximum power point tracking conditions MATLAB environment interconnected multiarea power systems

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whale optimisation algorithm for high-dimensional small-instance feature selection

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2021年第2期36卷 80-96页

作者： Mafarja, Majdi Jaber, Iyad Ahmed, Sobhi Thaher, Thaer Birzeit Univ Dept Comp Sci Birzeit Palestine An Najah Natl Univ Coll Engn & Informat Technol Nablus Palestine

In this paper, eight variants of the whale optimisation algorithm (WOA), that are based on eight different transfer functions, are introduced and used as search strategies in a wrapper feature selection model. Feature selection is a challenging task in machine learning process. It aims to minimise the size of a dataset by removing redundant and/or irrelevant features, with no information loss, to improve the efficiency of the learning algorithms. The used transfer functions belong to two different families;S-shaped and V-shaped. The proposed approaches have been tested on nine different high-dimensional medical datasets, with a low number of samples and multiple classes. The results revealed a superior performance for the V-shaped based approaches over the the S-shaped approaches. Moreover, the results of the V-shaped approach is compared with well-known feature selection approaches, and the superiority of the proposed approach is proven.

关键词： whale optimisation algorithm WOA transfer functions high-dimensional data classification feature selection

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A Chaotic Oppositional whale optimisation algorithm with Firefly Search for Medical Diagnostics

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Computers, Materials & Continua 2022年第7期72卷 959-982页

作者： Milan Tair Nebojsa Bacanin Miodrag Zivkovic K.Venkatachalam Singidunum University 32 Danijelova Str.11010BelgradeSerbia Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification *** study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning *** whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for *** Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous *** performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic *** of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

关键词： whale optimisation algorithm chaotic initialisation oppositionbased learning optimisation diagnostics

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Stacking ensemble learning fused with whale optimisation algorithm based method for crown prediction of hot-rolled strip

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IRONMAKING & STEELMAKING 2024年第4期51卷 281-296页

作者： Meng, Ling Ming Ding, Jing Guo Du, Haozhan Dong, Zi Shuo Gao, Peng Zhang, Dian Hua Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China Bengang Steel Plates Co Ltd Benxi Peoples R China

Precision of strip shape is one of the important indexes to measure the quality of strip steel in hot rolling. Accurate prediction of strip shape can realise the timely adjustment of the production system, which is the important foundation for ensuring the quality and stable production of hot-rolled strip steel products. Given the poor accuracy of the traditional mechanism model, this paper proposes a stacking ensemble learning for crown prediction that consist of the random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and linear regression, whose hyperparameters are optimised by whale optimisation algorithm (WOA) Firstly, the key features are selected by analysing the feature importance and the Pearson correlation coefficient between rolling parameters. Then, the hyperparameters of the stacking ensemble learning are optimised by four meta-heuristic algorithms during modeling process, among which WOA has the best optimisation effect. Compared to RF, XGBoost, LightGBM, CatBoost and the stacking, proposed method has the highest prediction accuracy that the hit rate reaches 98.58% in the range of 5.0 mu m of crown deviation. In addition, the effect of strip width, bending force, rolling force and roll shifting on crown are analysed based on Shapley additive explanations (SHAP).

关键词： hot-rolled strip strip crown prediction stacking ensemble learning whale optimisation algorithm

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Analysis of Energy Management Controller in Grid-Connected PV Wind Power System Coupled with Battery Using whale optimisation algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2022年第1期46卷 77-90页

作者： Natarajan, Sampathraja Loganathan, Ashok Kumar PSG Coll Technol Coimbatore 641004 Tamil Nadu India

Grid-connected renewable energy systems have advanced significantly in India recently. The most important renewable energy sources are solar and wind. The scheduling of power sources is critical to energy management and the system's economic benefit. The focus of this paper is on the energy management strategies of a grid-connected residential PV-wind generation system with battery storage. Time of Use (TOU) pricing is a cost-reflective electricity tariff method that has proven to be a successful methodology for reducing peak power demand in the domestic sector around the world, particularly in developed countries. The analysis of a whale optimisation algorithm-based energy management controller under TOU tariff for a grid-connected renewable energy system is proposed in this article. In the optimization, the fitness function is minimising the price of household electrical energy, and the optimisation variable is the dispatching ratio of electrical energy sold to the electrical network and consumed in the household. Using MATLAB, the performance of the proposed approach is compared to that of a conventional self-made for self-consumed and rest for sale (SFC&RFS) mode-based energy management controller. Both methods' daily benefit and revenue growth rate are compared to validate the efficacy of the proposed system. When compared to the SFC&RFS mode, the proposed WOA-based cost optimization offers a 10.95 percent increase in revenue growth. Maximum power point tracking has a significant impact on the efficiency of PV and wind power systems. In this paper, fuzzy logic is employed as an MPPT in a PV power system, and its effectiveness is compared to that of incremental conductance MPPT in terms of maximum power tracked, tracking time, and voltage quality.

关键词： PV Wind energy Energy management controller Time of Use tariff whale optimisation algorithm Self-made for self-consumed and rest for sale mode

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optimisation driven-based secure routing in MANET using atom whale optimisation algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2021年第1期27卷 77-99页

作者： Halhalli, Suresh R. Sugave, Shounak Rushikesh Jagdale, B. N. MS Bidve Engn Coll Elect Engn Latur India Dr Vishwanath Karad MIT World Peace Univ Sch CET Pune Maharashtra India

Mobile ad hoc networks (MANETs) routing is a very challenging task because of the dynamic nature of the network. The linking of provisional communication assures based on the infrastructure of MANET, however, there is no centralised monitoring process for making the routing in MANETs in terms of trust and security. Therefore, the stability routing is not considered, which may break easily in dynamic MANETs. Thus, this paper introduces a trust-based secure routing protocol using the proposed atom whale optimisation algorithm (AWOA), which is the trust-aware routing protocol. The developed atom whale optimisation is utilised to select the optimal route with respect to the trust factors, like average encounter rate (AER), and successful cooperation frequency (SCF), integrity factor, and the forwarding rate. Moreover, secure routing is performed between the nodes using the proposed AWOA. The AWOA is the integration of atom search optimisation (ASO), and whale optimisation algorithm (WOA) that inherits the faster global convergence. The fitness function is newly modelled considering mobility, and trust factors. The proposed AWOA outperformed other methods with a minimal end-to-end delay of 0.0083 sec, maximal packet delivery ratio (PDR) of 97.73%, and the maximal throughput of 85.05% respectively.

关键词： mobile ad hoc networks MANETs mobility average encounter rate AER whale optimisation algorithm WOA atom search optimisation ASO

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An improved whale optimisation algorithm for distributed assembly flow shop with crane transportation

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2021年第6期15卷 710-743页

作者： Li, Qing-Hua Li, Jun-Qing Zhang, Qing-Ke Duan, Peng Meng, Tao Shandong Normal Univ Sch Informat & Engn Jinan 250014 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Liaocheng Univ Sch Math Liaocheng 252059 Shandong Peoples R China

In this study, we investigate a classical distributed assembly flow shop scheduling problem with crane transportation. The objectives are to minimise the weighted value of the makespan and the energy consumptions. An improved whale optimisation algorithm (IWOA) which is embedded with a simulated annealing (SA) algorithm is proposed to solve the considered problem. First, a clustering method is applied to divide the solutions to improve the performance of the algorithm. Then, a right shift heuristic is developed to reduce the number of machine switches, therefore decreasing the energy consumption. In addition, two novel crossover operators, namely, factory crossover and solution crossover, are designed to increase the overall performance of the proposed algorithm. Furthermore, a SA-based global search heuristic is embedded in the algorithm to enhance its exploration abilities. Finally, several real-world instances were generated to test the performance of the proposed algorithm. The experimental results show that this algorithm performs better than other comparable algorithms.

关键词： distributed assembly flow shop scheduling crane energy consumptions whale optimisation algorithm

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whale optimisation algorithm tuned fractional order PI^λD^μ controller for load frequency control of multi-source power system

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第4期13卷 209-221页

作者： Kumar, Amit Suhag, Sathans Natl Inst Technol Elect Engn Dept Kurukshetra Haryana India

This paper deals with the design and optimisation of a fractional order (PID mu)-D-lambda (FOPID) controller implemented for load frequency control (LFC) of a multi-area multi-source power system having the nonlinearities of time delays, generation rate constraint (GRC) and governor dead band (GDB). whale optimisation algorithm (WOA), arguably for the first time in this area, is applied for optimal tuning of the parameters of FOPID controller. The comparative analysis of the performance of WOA vis-a-vis other recent evolutionary optimisation approaches is carried out from which it is observed that WOA optimised FOPID controller gives the best dynamic performance in terms of settling time and maximum overshoot in frequency and tie-line power deviations. Further, investigations carried out against system parametric variations within the range of +/- 25% reveal that WOA tuned FOPID controller is the most effective and robust against wide variations in system parameters.

关键词： fractional order controller generation rate constraint governor dead band load frequency control multi-source power system whale optimisation algorithm

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Improved whale optimisation algorithm for two-sided disassembly line balancing problems considering part characteristic indexes

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第8期60卷 2553-2571页

作者： Zhang, Yu Zhang, Zeqiang Guan, Chao Xu, Peiyu Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu Peoples R China

An effective method for disassembling large-sized waste products is to utilise two-sided disassembly lines. A mixed-integer programming model for two-sided disassembly lines is constructed in this study. The model considers four optimisation objectives: the number of mated-stations, idle index, demand index, and hazard index. The model redefines the hazard and demand indexes and adds new time constraints to the mated-station. In view of the complexity of the problem, a multi-objective improved whale algorithm is proposed, in which two different crossover operators are designed to simulate the predatory behaviour of whales to improve the efficiency of the solution. Then, a disturbance factor is introduced to reduce the probability of the population falling into a local optimum. The validity of the model and the effectiveness of the algorithm are verified by comparing the calculation results of the GUROBI solver with those of the proposed algorithm for two-sided disassembly line examples of different sizes. Subsequently, the algorithm is used to solve large-scale linear problems, and the results are compared with those of other algorithms to verify the superiority of the proposed algorithm. Finally, the model and algorithm are applied to a two-sided disassembly of an engine, and several optimal allocation schemes are obtained.

关键词： Two-sided disassembly line part characteristic indexes mixed-integer programming model whale optimisation algorithm multi-objective optimisation

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An efficient binary whale optimisation algorithm with optimum path forest for feature selection

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2020年第1-2期63卷 41-54页

作者： Samy, Ahmed Hosny, Khalid M. Zaied, Abdel-Naser H. Zagazig Univ Dept Informat Syst & Informat Technol Ash Sharqiyah Egypt

Feature selection is an essential process which aims to find the most representative features for image processing and computer vision applications where utilising selected features reduces required time for classification and increases the classification rate. In this study, a new binary whale optimisation algorithm for feature selection is proposed. This optimisation algorithm is based on whales' behaviour. The Optimum-Path Forest (OPF) technique is used as an objective function. This function is much faster than the other classification techniques. The proposed binary whale optimisation algorithm is evaluated using five datasets of colour images. The proposed algorithm outperformed existing optimisation algorithms such as Particle Swarm optimisation algorithm (PSOA), Firefly algorithm (FFA), Gravitational Search algorithm (GSA), Binary Harmony Search (BHS), Binary Clonal Flower Pollination algorithm (BCFA), Binary Cuckoo Search algorithm (BCSA), and Binary Bat algorithm (BBA) in terms of classification accuracy, number of selected features and execution times.

关键词： WOA whale optimisation algorithm OPF optimum-path forest feature selection meta-heuristic machine learning

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