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A novel approach in exergy optimization of a WWER1000 nuclear power plant using whale optimization algorithm

ANNALS OF NUCLEAR ENERGY 2020年 145卷 107540-107540页

作者： Ebrahimgol, H. Aghaie, M. Zolfaghari, A. Naserbegi, A. Shahid Beheshti Univ Engn Dept GC POB *** Tehran Iran

Nowadays, the scarcity of energy resources and the intensive demand of energy leads the researchers and scientists to find the efficient ways to save the energy and decrease the exergy loss. In this study, a new exergy optimization framework for a typical WWER1000 nuclear power plant (Bushehr NPP) is suggested. It is aimed to optimize the total efficiency of the power plant by decrease the exergy destruction using the first and second laws of thermodynamics. First, the main parameters of Bushehr NPP (BNPP) stream lines and exergy destruction of components are determined and the accuracy of the model is assessed. According to the analysis result, the reactor pressure vessel is the most inefficient equipment in the NPP and the steam generators are the next. Then, the exergy optimization of the steam cycle is evaluated using WOA (whale optimization algorithm). To investigate the performance of the WOA, four general fitness functions are determined and compared with GA and PSO and in next exergy optimization is compared. The total exergy destruction for Bushehr power plant is decreased from 1968.9MWth to 1914.7MWth and the overall thermal efficiency of the BNPP increases from 33.66% to 36.42% using WOA. The results demonstrate that the new algorithm WOA has a proper efficiency to implement in this optimization problem. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： WWER1000 optimization whale optimization algorithm Exergy destruction

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Automatic breast cancer detection based on optimized neural network using whale optimization algorithm

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021年第1期31卷 425-438页

作者： Fang, Hong Fan, Hongyu Lin, Shan Qing, Zhang Sheykhahmad, Fatima Rashid Dalian Univ Affiliated Zhongshan Hosp Dept Breast Dalian Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high-accuracy computing, computer-aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.

关键词： breast cancer computer-aided diagnosis image segmentation neural networks whale optimization algorithm

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HPCP-QCWOA: High Performance Clustering Protocol based on Quantum Clone whale optimization algorithm in Integrated Energy System

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 135卷 315-332页

作者： Liu, Yang Li, Chaoqun Zhang, Yao Xu, Mengying Xiao, Jing Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi 832000 Peoples R China Univ Cordilleras Baguio 2600 Philippines Beijing Univ Posts & Telecommun Beijing Peoples R China Xinjiang Tianfu Informat Technol Co Ltd E Shihezi 832000 Peoples R China

Combined with highly developed technologies such as cloud computing and the Internet of Things, the integrated energy system (IES) has realized the coordinated scheduling and utilization of various energy resources with its superiority of comprehensive control and high applicability. Meanwhile, approaches to enhance the quality of service (QoS), e.g. network lifetime, energy distribution, and data transmission delay, of an indispensable component of IES, i.e., integrated energy system wireless sensor networks (IESWSNs), have become an urgent research issue. To tackle the issue, a high-performance clustering protocol termed quantum clone whale optimization algorithm (HPCP-QCWOA) is proposed for IESWSNs. By designing a new quantum operator and a new layering clone operator, HPCP-QCWOA can find an approximate optimal clustering scheme to improve the QoS performance of IESWSNs. Unlike other population-based clustering protocols, a new objective function that embraces remaining energy, intra-cluster distance, BS distance, and data transmission delay is designed in the HPCP-QCWOA, which renders its clustering performance more desirable than some other state-of-the-art clustering protocols, i.e., O-LEACH, LDIWPSO, and ARSH-FATI-CHS. Empirically, simulation experiments under different scenarios illustrated that the lifetime of the HPCP-QCWOA-based clustering has increased by 28.78%, 25.50% and 11.22% respectively compared with O-LEACH, LDIWPSO, and ARSH-FATI-CHS. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Integrated energy system Wireless sensor network Clustering Quality of service whale optimization algorithm Quantum optimization

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Cosine adapted modified whale optimization algorithm for control of switched reluctance motor

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COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 978-1017页

作者： Saha, Nutan Panda, Sidhartha VSSUT Dept Elect Engn Burla 768018 Odisha India

whale optimization algorithm (WOA) imitates social conduct of humpback whales which is inspired by bubble net hunting strategy of humpback whales. In the present study, Cosine adapted modified whale optimization algorithm (CamWOA) which is a modified version of WOA, has been proposed where cosine function is incorporated for the selection of control parameter "d" which governs the position of whales during optimization process. Also, correction factors are employed to modify the movement of search agents during the search process. These changes provide a proper balance between exploration and exploitation phases in CamWOA technique. The performance of CamWOA is analyzed by testing on a set of benchmark functions and compared with other state-of-the-art algorithms. It is observed that CamWOA outperforms other state-of-the-art metaheuristic algorithms in majority of benchmark functions. The efficiency of CamWOA is also evaluated by solving a multiobjective engineering problem pertaining to control of switched reluctance motor. The simulation results confirm that CamWOA yields very promising and competitive results compared to that of WOA and other metaheuristic optimization algorithms.

关键词： cosine adapted modified whale optimization algorithm proportional integral controller switch reluctance motor torque ripple whale optimization algorithm

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Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年第0期89卷 103457-000页

作者： Long, Wen Wu, Tiebin Jiao, Jianjun Tang, Mingzhu Xu, Ming Guizhou Univ Finance & Econ Key Lab Econ Syst Simulat Guiyang 550025 Guizhou Peoples R China Hunan Univ Humanities Sci Technol Dept Energy & Elect Engn Loudi 417000 Hunan Peoples R China Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Guizhou Peoples R China Changsha Univ Sci & Technol Sch Energy Power & Engn Changsha 410114 Hunan Peoples R China

whale optimization algorithm (WOA) is a relatively new meta-heuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving high-dimensional optimization problems. The proposed RLWOA adopts a modified conversion parameter update rule that relies on Logistic model to balance between diversity and convergence during the search process, and a new refraction-learning strategy based on the principle of refraction of light is proposed to help the population jump out of a local optimum. The experiments on a set of benchmark test functions with various features, i.e., 12 widely used benchmark functions with 100, 1000, and 10000 dimensions, two practical engineering design problems, and parameter estimation problem of photovoltaic model. The comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other state-of-the-art meta-heuristic algorithms for solving high-dimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.

关键词： whale optimization algorithm Refraction learning High-dimensional optimization Engineering design optimization Photovoltaic model Parameter estimation

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Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第8期80卷 12435-12468页

作者： Abd Elaziz, Mohamed Nabil, Neggaz Moghdani, Reza Ewees, Ahmed A. Cuevas, Erik Lu, Songfeng Zagazig Univ Dept Math Fac Sci Zagazig Egypt Univ Sci & Technol Oran Mohammed Boudiaf USTO MB Fac Math & Informat Dept InformatLab SIMPA BP 1505 El Mnaouer 31000 Oran Algeria Persian Gulf Univ Ind Management Dept Boushehr Iran Damietta Univ Dept Comp Dumyat Egypt Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

关键词： Image segmentation Multilevel thresholding Swarm algorithm Volleyball premier league algorithm whale optimization algorithm

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Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators

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APPLIED INTELLIGENCE 2021年第8期51卷 5358-5387页

作者： Ghasemi, Mohsen Bagherifard, Karamollah Parvin, Hamid Nejatian, Samad Pho, Kim-Hung Islamic Azad Univ Yasooj Branch Dept Comp Engn Yasuj Iran Islamic Azad Univ Yasooj Branch Young Researchers & Elite Club Yasuj Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Informat Technol Da Nang 550000 Vietnam Islamic Azad Univ Dept Comp Engn Nourabad Mamasani Branch Nourabad Mamasani Iran Islamic Azad Univ Yasooj Branch Dept Elect Engn Yasuj Iran Ton Duc Thang Univ Fract Calculus Optimizat & Algebra Res Grp Fac Math & Stat Ho Chi Minh City Vietnam

Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced.

关键词： Next release problem Grey wolf optimizer whale optimization algorithm Fairness quality indicator Uncertainty size

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Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19959-20000页

作者： Wang, Fengxian Feng, Shaozhi Pan, Youmei Zhang, Huanlong Bi, Senlin Zhang, Jiaxiang Zhengzhou Univ Light Ind Coll Elect & Informat Engn Ke Xue Ave Zhengzhou 450002 Henan Peoples R China

To address the problems of slow convergence of the traditional whale optimization algorithm and the tendency to fall into local optimal solutions, an improved algorithm based on a combination of elite disturbance opposition-based learning and dynamic spiral updating (OWOA) is proposed, which improves the convergence speed of the algorithm and increases the probability of the algorithm jumping out of the local optimum to find the global optimum. First, an elite disturbance opposition-based learning strategy is used to initialize the whale population to ensure diversity of the population, and then, random perturbation of the elite whales increases the exploratory capacity of the population to avoid falling into local optimal solutions. Secondly, a dynamic spiral update strategy is used to dynamically adjust the shape of the spiral with the number of iterations, avoiding the algorithm from falling into a local optimum at the beginning of the iteration and improving the convergence speed of the algorithm at a later stage, which improves the ability of the algorithm to find the global optimum. The experimental results show that the OWOA algorithm, due to the introduction of the lite disturbance opposition-based learning strategy and dynamic spiral updating strategy, has higher convergence speed and accuracy on single-peak functions, multi-peak functions and multi-dimensional functions compared to other algorithms. Furthermore, good results have been obtained in the practical application of OPF. This shows that our improvements are effective.

关键词： whale optimization algorithm Opposition-based learning Dynamic spiral updating Intelligent optimization algorithm Optimum power flow

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An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32169-32194页

作者： Rahnema, Nouria Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms.

关键词： Artificial bee colony algorithm whale optimization algorithm Data clustering optimization

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Application of improved hybrid whale optimization algorithm to optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第17期35卷 12433-12451页

作者： Uzer, Mustafa Serter Inan, Onur Selcuk Univ Ilgin Vocat Sch Elect & Automat Konya Turkiye Selcuk Univ Fac Technol Comp Engn Konya Turkiye

The whale optimization algorithm (WOA) is one of the recent meta-heuristic algorithms. WOA has advantages such as an exploration mechanism that leads towards the global optimum, a suitable balance between exploration and exploitation that avoids the local optimum, and a very good exploitation capability. In this study, five new hybrid algorithms are proposed to develop these advantages. Two of them are developed by combining WOA and Particle Swarm optimization (PSO) algorithms, and three of them are developed by adding the Levy flight algorithm to this combination in different ways. The proposed algorithms have been tested with 23 mathematical optimization problems, and in order to make a more accurate comparison, the average optimization results and corresponding standard deviation results are calculated by running these algorithms 30 times for each optimization problem. The proposed algorithms' performances were evaluated among themselves, and the WOALFVWPSO algorithm performed better among these algorithms. This proposed algorithm has been first compared with WOA and PSO, then with other algorithms in the literature. According to WOA and PSO, the proposed algorithm performs better in 19 of 23 mathematical optimization problems, and according to other literature, it performs better in 15 of 23 problems. Also, the proposed algorithm has been applied to the pressure vessel design engineering problem and achieved the best result compared to other algorithms in the literature. It has been proven that the WOALFVWPSO algorithm provides competitive solutions for most optimization problems when compared to meta-heuristic algorithms in the literature.

关键词： whale optimization algorithm Particle swarm optimization Levy flight optimization problems Benchmark functions

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