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Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators

APPLIED SOFT COMPUTING 2020年 86卷

作者： Qais, Mohammed H. Hasanien, Hany M. Alghuwainem, Saad King Saud Univ Fac Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

This paper proposes an enhancement of the meta-heuristic whale optimization algorithm (WOA) for maximum power point tracking (MPPT) of variable-speed wind generators. First of all, twenty-three benchmark functions tested the enhanced whale optimization algorithm (EWOA). Then the statistical results of EWOA compared with the results of other algorithms (WOA, salp swarm algorithm (SSA), enhanced SSA (ESSA), grey wolf optimizer (GWO), augmented GWO (AGWO), and particle swarm optimization (PSO). Also, the non-parametric statistical test and convergence curves proved the superiority and the speed of the EWOA. After that, the EWOA and WOA are implemented to design optimal Takagi-Sugeno fuzzy logic controllers (FLCs) to enhance the MPPT control of variable-speed wind generators. Moreover, real wind speed data has confirmed the robustness of optimal EWOA-MPPT. In conclusion, the simulation results revealed that the EWOA is a promising algorithm to be applied for solving different engineering problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Benchmark functions optimization methods whale optimization algorithm Wind generator

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An Efficient-Assembler whale optimization algorithm for DNA Fragment Assembly Problem: Analysis and Validations

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IEEE ACCESS 2020年 8卷 222144-222167页

作者： Abdel-Basset, Mohamed Mohamed, Reda Sallam, Karam M. Chakrabortty, Ripon K. Ryan, Michael J. Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt UNSW Canberra Australian Def Force Acad Sch Engn & Informat Technol Capabil Syst Ctr Campbell ACT 2612 Australia

The study of deoxyribonucleic acid (DNA) is crucial in many fields, including medicine, biology, zoology, agriculture, and forensics. Since reading a DNA sequence is onerous because of its massive length, it is common in many DNA analysis applications to divide DNA strands into small segments or fragments which, after analysis, must be reassembled. Since this reassembly takes a non-specific polynomial time to solve, the DNA fragment assembly problem (DFAP) is NP-hard. This paper proposes a new assembler for tackling the DFAP based on the overlap-layout-consensus (OLC) approach. The proposed assembler adapts a discrete whale optimization algorithm (DWOA) using standard operators adopted from evolutionary algorithms to simulate the strategy adopted by humpback whales when searching for prey. For the first time, we formulate the behaviors of whales to be applied directly to any discrete optimization problem based on three primary operations: a swap-based best-position operator, an ordered crossover operator, and selection of a random whale operation to perform the exploitation and exploration phases of the algorithm. These operations were carefully designed to preserve the methodology of the original whale algorithm. DFAP is a multi-objective problem that seeks to reach the optimal order of segments that maximizes the overlap score and minimizes the number of contigs (set of overlapping DNA segments) to compose a one-contig DNA strand. Existing local search methods, such as problem aware local search (PALS) many non-conflicting movements (PALS2-many), suffer from being trapped in local optima. Hence, the integration of DWOA with PALS2-many improves the search capability for finding the optimal order of fragments. In addition, we propose a new variation of PALS2-many that achieves simultaneously the two objectives of DFAP. Our proposed DWOA was compared with a number of the most recent robust assemblers: a hybrid crow search algorithm for solving the DFAP (CSA-P2M*Fit),

关键词： DNA optimization Standards whales Search problems Genetic algorithms Heuristic algorithms DNA sequence DNA fragments assembly problem overlap-layout-consensus whale optimization algorithm

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Energy management strategy for long-life fuel cell hybrid power systems based on improved whale optimization algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 1-16页

作者： Fu, Zhichao Chen, Qihong Zhou, Ze Zhao, Dongqi Yu, Shuiying Zhang, Liyan Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Wuhan Inst Marine Elect Prop Fuel Cell Div Wuhan Peoples R China Wuhan Inst Shipbuilding Technol Fac Comp & Informat Technol Wuhan Peoples R China

Systems primarily powered by hydrogen fuel cells (FC) have vast application prospects in fields such as automobiles and power generation systems. Extending the useful life of FC systems has been an important goal to promote FC applications. In this paper, a long-life energy management strategy (EMS) is proposed for an FC hybrid power system (FCHPS) consisting of two FCs and one lithium battery (LB). Based on the analysis of FC life degradation characteristics and the dynamic power variations, an FC life degradation model for power optimization is established. Further, a long-life objective with constraints is constructed to optimize the power allocation between two FCs and one LB. To overcome the inefficiency of the traditional whale optimization algorithm (WOA) at the late stage of solving the optimization problem, an improved WOA (IWOA) algorithm based on a linear decay coefficient adjustment is designed to strengthen the search capability. Then, the feasibility and effectiveness of the proposed IWOA are verified in the simulation results. Finally, the experimental results show a 2.26% decrease in life degradation over 24 hours compared with the logic threshold strategy when employing the proposed long-life EMS integrated with IWOA.

关键词： Fuel cells useful life energy management strategy life degradation whale optimization algorithm

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An enhanced whale optimization algorithm for large scale optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 233卷 107543-107543页

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

whale optimization algorithm was developed based on the prey-catching characteristics of the humpback whales. Due to its simple structure and efficiency, the researchers employed the algorithm to address numerous disciplines' numerous problems. The profound analysis of the whale optimization algorithm discloses that the algorithm suffers from low exploration ability, lesser accuracy, and early convergence. Additionally, performance of the whale optimization algorithm and most of its variants in high-dimensional optimization problems is not satisfactory. This study proposes a new variant with several modifications to the basic whale optimization algorithm to solve high-dimensional problems. A unique selection parameter is introduced in the whale optimization algorithm to balance the algorithm's global and local search phase. The co-efficient vectors A and C are modified and used effectively to explore and exploit the search region better. In the exploration phase, random movement is allowed to reduce the computational burden of the algorithm. An inertia weight is introduced in the exploitation phase for exhaustive search nearby the best solution. The proposed algorithm evaluates twenty-five benchmark functions using dimensions 100, 500, 1000, and 2000 and compared the results with the whale optimization algorithm and its variants. The estimated outcomes are also compared with seven basic metaheuristic algorithms. Finally, statistical analysis, complexity analysis, and convergence analysis are performed to establish the algorithm's efficacy. All the test result suggests better performance of the proposed algorithm on higher-dimensional problems. (c) 2021 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm High dimensional problem Benchmark function Friedman's test Nemenyi multiple comparison tests Boxplot

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ENERGIES 2017年第7期10卷 874-874页

作者： Zhao, Haoran Guo, Sen Zhao, Huiru North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

Accurate and reliable forecasting on energy-related carbon dioxide (CO2) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO2 emissions with its driving forces, the accurate forecasting for CO2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method. Taking China's CO2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application.

关键词： CO2 emissions prediction whale optimization algorithm LSSVM parameters optimization driving forces

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Three-dimensional path planning for autonomous underwater vehicles based on a whale optimization algorithm

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OCEAN ENGINEERING 2022年 250卷 111070-111070页

作者： Yan, Zheping Zhang, Jinzhong Zeng, Jia Tang, Jialing Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

Due to the influence of the complex underwater environment, safety, smoothness, real-time requirements and poor integrity, the path of autonomous underwater vehicles is mainly determined by perception, decisionmaking and control systems. These not only achieve object detection and environmental investigation but also ensure the safety and reliability of autonomous navigation systems. This paper proposes a whale optimization algorithm based on forward-looking sonar to tackle the three-dimensional path planning of autonomous underwater vehicles. The purpose of path planning is to effectively avoid threatened regions and obtain the shortest and safest paths with minimal fuel and time. The whale optimization algorithm based upon bubble-net hunting behavior imitates the contraction surrounding mechanism, logarithmic spiral position updating mechanism and stochastic searching mechanism to effectively solve the complex problem in the solution space. The whale optimization algorithm not only avoids premature convergence and avoids falling into the local optimum to achieve the global optimal solution but also utilizes exploration and exploitation to enhance the convergence speed and calculation accuracy. To verify the superiority and overall optimization performance of the whale optimization algorithm, the optimization results of the algorithm are compared with other algorithms by minimizing the fitness value. The experimental results reveal that the whale optimization algorithm has better planning efficiency, shorter execution time, faster convergence speed and higher solution precision, which is effective and efficient for path planning.

关键词： whale optimization algorithm Forward looking sonar Autonomous underwater vehicle Path planning Experimental results

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Galactic swarm-improved whale optimization algorithm-based resource management in Internet of things

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第3期35卷 e5006-e5006页

作者： Karthick, Subramani Gomathi, Nandagopal Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai 600062 Tamil Nadu India

Internet of things (IoT) is the reliable alternative among the networking technologies for achieving high performance with improved potentialities of flexible adoptions, data exchanges, resource allocations, and system controls. The existing IoT suffers from the limitations of resource allocation ranging between complicated service provisioning environments and networking service quality mismatching. IoT environment needs to handle the resource allocation issue for attaining satisfactory degree of quality of experience (QoE) that maps multiple resources to gateways. This problem of mapping multiple resources to gateways belongs to the class of NP-complete problem and can be ideally solved through intelligent metaheuristic algorithms. In this paper, galactic swarm-improved whale optimization algorithm-based resource management (GSIWOA-RM) scheme is proposed for efficient mapping of multiple resources to gateways in IoT. It specifically utilizes galactic swarm optimization algorithm (GSOA) for establishing global control with inherited multiple adaptive cycles of exploitation and exploration during resource allocation. It further prevents the limitations of early convergence in the exploitation phase by utilizing the evolution-based whale optimization algorithm (WOA) that aids in better balance between exploitation and exploration. The simulation results of the proposed GSIWOA-RM scheme confirm a better throughput of 28.32% with minimized delay and energy consumptions of 19.24% and 21.82%, when compared to the baseline resource management schemes.

关键词： delay galactic swarm optimization gateway Internet of things (IoT) resource allocation whale optimization algorithm

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A whale optimization algorithm based on quadratic interpolation for high-dimensional global optimization problems

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APPLIED SOFT COMPUTING 2019年 85卷 105744-000页

作者： Sun, Yongjun Yang, Tong Liu, Zujun Xidian Univ State Key Lab Integrated Serv Network 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

whale optimization algorithm (WOA), as a new population-based optimization algorithm, performs well in solving optimization problems. However, when tackling high-dimensional global optimization problems, WOA tends to fall into local optimal solutions and has slow convergence rate and low solution accuracy. To address these problems, a whale optimization algorithm based on quadratic interpolation (QIWOA) is presented. On the one hand, a modified exploration process by introducing a new parameter is proposed to efficiently search the regions and deal with the premature convergence problem. On the other hand, quadratic interpolation around the best search agent helps QIWOA to improve the exploitation ability and the solution accuracy. Moreover, the algorithm tries to make a balance between exploitation and exploration. QIWOA is compared with several state-of-the-art algorithms on 30 high-dimensional benchmark functions with dimensions ranging from 100 to 2000. The experimental results show that QIWOA has faster convergence rate and higher solution accuracy than both WOA and other population-based algorithms. For functions with a flat or sharp bottom, QIWOA is difficult to find the global optimum, but it still performs best compared with other algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： High-dimensional optimization Quadratic interpolation whale optimization algorithm

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Bridge Condition Deterioration Prediction Using the whale optimization algorithm and Extreme Learning Machine

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BUILDINGS 2023年第11期13卷

作者： Jiang, Liming Tang, Qizhi Jiang, Yan Cao, Huaisong Xu, Zhe Chongqing Jiaotong Univ State Key Lab Mt Bridge & Tunnel Engn Chongqing 400074 Peoples R China Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China Chongqing Yuhe Expressway Co Ltd Chongqing 400799 Peoples R China Guangxi Nanbai Expressway Co Ltd Nanning 530029 Peoples R China

To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model's exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance.

关键词： bridge engineering inspection data deterioration condition prediction whale optimization algorithm extreme learning

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Multi-threshold image segmentation based on an improved whale optimization algorithm: A case study of Lupus Nephritis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期96卷

作者： Shi, Jinge Chen, Yi Cai, Zhennao Heidari, Ali Asghar Chen, Huiling Chen, Xiaowei Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Med Univ Affiliated Hosp 1 Dept Rheumatol & Immunol Wenzhou 325000 Peoples R China

Lupus nephritis (LN) is a severe renal complication of systemic lupus erythematosus (SLE), characterized by kidney inflammation and damage, often leading to significant morbidity and mortality. Thus, aiding clinicians in the timely diagnosis of LN is imperative, with medical imaging serving as an essential adjunctive diagnostic tool.. Image segmentation techniques discriminate various tissues and structures within medical images. This capability allows for identifying and delineating specific regions within images, facilitating precise localization and characterization of anomalies or pathological conditions to aid physicians in their diagnosis. Multi-threshold Image Segmentation (MTIS) is widely recognized for its direct and practical application in medical image segmentation. Hence, this study introduces an enhanced whale optimization algorithm (CVWOA) to enhance the accuracy of image segmentation. The proposed CVWOA combines convex local directed search and vortex rotation mechanisms, significantly improving the algorithm's overall optimization performance. To evaluate the performance of CVWOA, experiments were conducted using CVWOA on benchmark datasets, and comparisons were made with classical and state-of-the-art algorithms. Building upon these findings, the study proposes a CVWOA-based MTIS approach. This method utilizes non-local means two-dimensional histograms in conjunction with Renyi's entropy to perform segmentation on the Berkeley Segmentation Dataset and the LN segmentation dataset. Experimental results demonstrate that this segmentation method is both effective and competitively positioned.

关键词： whale optimization algorithm Image segmentation 2DRenyi's entropy Non-local means 2D histogram Lupus Nephritis

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