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Improved whale optimization algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System

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ENERGIES 2022年第14期15卷 5037页

作者： Habib, Salman Abbas, Ghulam Jumani, Touqeer A. Bhutto, Aqeel Ahmed Mirsaeidi, Sohrab Ahmed, Emad M. Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs 66020 Pakistan Mehran Univ Engn & Technol Dept Mech Engn SZAB Campus Khairpur Mirs 66020 Pakistan Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Saudi Arabia Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (K-P), integral (K-I) and derivative (K-D) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler-Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from -50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics.

关键词： whale optimization algorithm improved whale optimization algorithm automatic voltage regulator stability dynamic response enhancement

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A modified whale optimization algorithm to overcome delayed convergence in artificial neural networks

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SOFT COMPUTING 2021年第15期25卷 10275-10286页

作者： Kushwah, Rashmi Kaushik, Manika Chugh, Kashish Jaypee Inst Informat Technol Noida India

Artificial neural network (ANN) is modeled to predict and classify problems. However, in the training phase of ANNs discovering faultless values of the weights of a network is extremely troublesome. Traditional weight updating methods often get stuck into local optima and converge to optimal solutions very slowly. Therefore, to overcome these drawbacks a modified version of a nature-based algorithm which merges meta-heuristics with weight-updating technique of ANN has been used in this paper. whale optimization algorithm (WOA) is a well-established, efficient and competitive algorithm inspired by the hunting mechanism of the whales including their behavior in finding and attacking their prey with their bubble-net feeding technique. In WOA, the next location of the search individuals or whales is modified depending on some probability. Due to the high exploration rate of WOA, there is a disproportion between exploration and exploitation in the WOA and it also converges to the solution slowly. Thus, to establish an equilibrium between exploration and exploitation a new variant of WOA called modified whale optimization algorithm (MWOA) is proposed to overcome the problem of delayed convergence. In MWOA, roulette wheel selection is combined with WOA to enhance the convergence speed of WOA. MWOA is tested on 11 benchmark functions, and the outcomes are compared with WOA. The results prove that MWOA has gained success in overcoming the problem of the slow convergence of WOA. Also, the results show that the proposed MWOA technique, when applied to ANN, can overcome the problems of traditional techniques and has improved the results.

关键词： Artificial neural network whale optimization algorithm Meta-heuristic Roulette wheel selection

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New network interface selection based on MADM and multi-objective whale optimization algorithm in heterogeneous wireless networks

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 3580-3615页

作者： Mefgouda, Brahim Idoudi, Hanen Univ Manouba Natl Sch Comp Sci Manouba Tunisia

Multi-attribute decision-making (MADM) approaches are commonly used to model and solve the network interface selection problem in heterogeneous wireless networks. Despite their importance and advantages to deal with this issue, they suffer from the rank reversal problem (RRP) and the selection of the highest-ranking score network without considering the user's/service requirements. In this paper, a novel method is introduced to solve the MADM limitations. Besides, the weights assignment technique is modelled as a multi-objective problem. Then, an extended version of the whale optimization algorithm is applied to obtain the suitable weights of the decision criteria. The obtained results showed that applying the developed technique with MADM approaches reduces (sometimes avoids completely) the RRP by an average up to 94%, compared to Analytical Hierarchy Process. It also allows meeting the user's/service requirements by optimizing data rate and packet loss, for streaming services, by an average up to 14.3 (kbps) and 20 x 10(6) (ms), respectively.

关键词： Network interface selection Heterogeneous wireless networks Multi-attribute decision making whale optimization algorithm Rank reversal problem Quality of service

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Grey Wolf Optimizer and whale optimization algorithm for Stochastic Inventory Management of Reusable Products in a Two-Level Supply Chain

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IEEE ACCESS 2023年 11卷 40278-40297页

作者： Sadeghi, Amir Hossein Bani, Erfan Amani Fallahi, Ali Handfield, Robert North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27606 USA Sharif Univ Technol Dept Ind Engn Tehran *** Iran North Carolina State Univ Dept Business Management Raleigh NC 27607 USA

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.

关键词： Supply chains Inventory management Uncertainty Stochastic processes Metaheuristics whale optimization algorithms Programming Reuse and recovery chance-constrained programming grey wolf optimizer whale optimization algorithm Taguchi method

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A balanced whale optimization algorithm for constrained engineering design problems

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APPLIED MATHEMATICAL MODELLING 2019年 71卷 45-59页

作者： Chen, Huiling Xu, Yueting Wang, Mingjing Zhao, Xuehua Wenzhou Univ Dept Comp Sci Wenzhou 325035 Peoples R China Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China

In this study, two novel effective strategies composed of Levy flight and chaotic local search are synchronously introduced into the whale optimization algorithm (WOA) to guide the swarm and further promote the harmony between the inclusive exploratory and neighborhood-informed capacities of the conventional technique and investigate the core searching capabilities of WOA in dealing with optimization tasks. However, the conventional WOA may simply be stuck at local optima or the global best may not be obtained successfully when tackling more complex optimization landscapes, including the multimodal and high dimensional scenarios. To substantiate the efficacy of the enhanced method, it is compared to a set of well-regarded variants of particle swarm optimization and differential evolution. The used benchmark problems are composed of unimodal, multimodal, and fixed-dimensions multimodal functions. Additionally, the proposed balanced method is applied to realize three practical, well-known mathematical models such as tension/compression spring, welded beam, pressure vessel design, three-bar truss design, and I-beam design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitors in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios. (C) 2019 Published by Elsevier Inc.

关键词： whale optimization algorithm Levy flight Chaotic local search Fixed-dimension functions Complex optimization tasks Welded beam

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A chaotic strategy-based quadratic Opposition-Based Learning adaptive variable-speed whale optimization algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年第0期193卷 71-99页

作者： Li, Maodong Xu, Guanghui Lai, Qiang Chen, Jie Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Jiangxi Peoples R China Hubei Univ Technol Sch Sci Wuhan 430068 Hubei Peoples R China

In this paper, a chaotic strategy-based quadratic opposition-based learning adaptive variable-speed whale optimization algorithm is proposed. The improved algorithm is used to solve the problems that the whale optimization algorithm's convergence accuracy and convergence speed are insufficient. First, the whale population is initialized by a combination of chaotic maps and quadratic opposition-based learning to obtain a population with better fitness. Then, by introducing an adaptive variable speed adjustment factor, each search link of the algorithm forms a negative feedback adjustment network, thereby effectively balancing the algorithm's exploration ability and exploitation ability. Finally, 20 benchmark test functions and 3 complex constrained engineering optimization problems were used to conduct extensive tests on the improved algorithm. The test results show that the improved algorithm has faster convergence speed and higher convergence accuracy, and can effectively jump out of the local optimum. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Quadratic opposition-based learning Adaptive variable speed adjustment Chaos strategy Negative feedback regulation

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HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期37卷 52-104页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Chakraborty, Ratul Saha, Moumita Nama, Sukanta Natl Inst Technol Dept Comp Sci & Engn Agartala Tripura India Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Dept Math Agartala 799046 Tripura India Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India Directorate Informat Technol Agartala Tripura India Maharaja Bir Bikram Univ Dept Appl Math Agartala Tripura India

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the whale optimization algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.

关键词： benchmark functions hunger games search algorithm IEEE CEC 2019 functions Nemenyi multiple comparison test whale optimization algorithm

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Illumination correction via support vector regression based on improved whale optimization algorithm

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COLOR RESEARCH AND APPLICATION 2021年第2期46卷 303-318页

作者： Wang, Chao Zhu, Zefei Chen, Sheng Yang, Junyi Hangzhou Dianzi Univ Sch Mech Engn Hangzhou 310018 Peoples R China

Variations in illumination lead to serious errors in the evaluation of chromatic aberration. We propose an illumination correction model based on the opposition-based learning improved whale optimization algorithm for support vector regression optimization, named "OBL-IWOA-SVR." Because the initial population quality of the whale optimization algorithm has a significant impact on the solution speed and accuracy, the Opposition-based learning strategy is adopted in this article to mix the original population and its opposite individuals and select the best as the new population, replacing the random initialization to generate a more suitable initial population. This increases the diversity of the population and thus overcomes the impact of the quality of the initial population on the algorithm performance. Secondly, the proposed algorithm adopts the adaptive weight and the convergence factor based on the variation of the cosine law to balance the algorithm's global exploration ability and local development ability and enhance the convergence accuracy. Finally, the algorithm utilizes good global searching ability to optimize the penalty factor and nuclear parameters and obtains the optimal support vector machine parameter combination to construct the illumination correction model OBL-IWOA-SVR accurately and robustly. Experimental results show that the illumination correction model proposed in this article is found superior to other models in significance analysis: the root mean square error of the proposed model is 0.0173, the smallest of all illumination correction models. Furthermore, the model exhibits good stability and high illumination estimation accuracy.

关键词： illumination correction opposition‐ based learning support vector regression whale optimization algorithm

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Health-Aware Battery-Fast-Charging Strategy Using Thermal-Aging Cell Model and whale optimization algorithm

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ENERGY TECHNOLOGY 2024年第1期12卷 2300861-2300861页

作者： Bose, Bibaswan Teja, Saladi Sairam Garg, Akhil Gao, Liang Li, Wei Singh, Surinder Babu, B. Chitti Indian Inst Technol Ctr Automot Res & Tribol Delhi India Huazhong Univ Sci & Technol Sch Mech Sci & Engn Huazhong Peoples R China Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China Swinburne Univ Technol Fac Sci Engn & Technol Dept Mech Engn & Prod Design Engn Hawthorn Australia Indian Inst Informat Technol Design & Mfg Dept Elect & Commun Engn Chennai Tamil Nadu India

In this article, developing an optimized health-aware battery-fast-charging strategy is proposed using multistep constant-current constant-voltage (MSCCCV)-charging technique. First, the thermal-aging cell model (TACM) is formulated utilizing a 1D radial heat-conduction phenomenon. The utilization of this model facilitates the generation of a simulated cell model, thereby mitigating the necessity for conducting multiple experimental assays. A cycle life predictor based on multi-input elastic net regression is then developed, which forecast cell's cycle life based on inputs from TACM. The accuracy of the projected life is 87% which is validated using APR18650M1B cell cycling dataset. This is then utilized to devise an adaptive MSCCCV-charging strategy with four-step constant-current (CC), which is optimized using whale optimization algorithm, have C-rate of 5.2C, 4.4C, 5.2C, and 4.52C. An alternate cell (LGEBM26R) is used to validate the reproducibility of the proposed charging method. The second cell charging is optimized using the aforesaid steps and four-step CC, thus obtained have C-rate of 1.3C, 1.95C, 2.23C, and 1.24C. Compared to 1C-CCCV charging, the algorithm increases LGEBM26R cell cycle life by 17.23% and APR18650M1B cell cycle life by 28%. The MSCCCV technique's superiority is demonstrated through a comparison with benchmark techniques considering charging time and cycle life as performance parameters. In this article, an optimized health-aware battery-fast-charging approach is proposed utilizing multistep constant-current constant-voltage technology. Initially, thermal-aging cell model (TACM) is developed using a 1D-radial heat-conduction phenomena to create a simulated cell model. A multi-input elastic net regression cycle life predictor is developed using TACM inputs. An adaptive four-step-CCCV-charging technique is devised, which is optimized utilizing whale optimization algorithm. Hypothesis is validated by optimizing charging strategy of alternalte

关键词： cycle life predictions elastic net regressions MSCCCVs thermal-aging cell models whale optimization algorithm

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Parameter extraction of solar photovoltaic models by means of a hybrid differential evolution with whale optimization algorithm

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SOLAR ENERGY 2018年 176卷 742-761页

作者： Xiong, Guojiang Zhang, Jing Yuan, Xufeng Shi, Dongyuan He, Yu Yao, Gang Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Guizhou Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China Guizhou Elect Power Grid Dispatching & Control Ct Guiyang 550002 Guizhou Peoples R China

Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. The original differential evolution (DE) is good at exploring the search space and locating the region of global optimum, but it is slow at exploitation of the solutions. Quite the opposite, the original whale optimization algorithm (WOA) is good at exploiting the population information, but it easily suffers from premature convergence. In such a context, in this paper, an effective hybrid method named DE/WOA by combining the exploration of DE with the exploitation of WOA is proposed for extracting the accurate parameters of PV models. A set of 13 numerical benchmark functions with different characteristics is firstly employed to verify the performance of DE/WOA. Then, DE/WOA is applied to parameter extraction of three PV models, i.e., single diode, double diode, and PV module models. Finally, DE/WOA is implemented to a practical PV power station in the Guizhou Power Grid of China to further validate its effectiveness under different irradiances, temperatures, and dynamic weather conditions. All the experimental results comprehensively demonstrate that DE/WOA performs significantly better than the original DE, WOA, and five advanced variants of them and is highly competitive with some recently-proposed parameter extraction methods.

关键词： Solar photovoltaic Parameter extraction Differential evolution whale optimization algorithm

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