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An Enhanced beluga whale optimization algorithm for Engineering optimization Problems

JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING 2024年 1-38页

作者： Punia, Parul Raj, Amit Kumar, Pawan Cent Univ Haryana Dept Math Mahendergarh 123031 Haryana India

This paper presents an enhanced beluga whale optimization algorithm (EBWOA) for engineering optimization problems. To enhance the performance and address the challenges of poor convergence and suboptimal solution stagnation commonly faced by the original beluga whale optimization algorithm (BWOA), EBWOA employs a two-step approach. In the initial stage, a dynamic update factor is introduced to accelerate convergence during the exploitation phase of BWOA. Subsequently, the second stage incorporates the Cauchy mutation operator to inject diversity into the population, preventing it from becoming entrapped in local optima. The proposed enhancement is validated on 15 classical benchmark functions and CEC-19 functions in terms of solution quality and convergence speed. To assess the efficiency of EBWOA, the algorithm is applied to a real-world industrial problem, specifically, the spiral steel pipe manufacturing system (SSPM), serving as a case study and four classical engineering design optimization problems. The simulation results demonstrated the superiority of EBWOA in optimizing the fuzzy availability of the SSPM industrial system and successfully solving all four constrained engineering design problems when compared to other recent metaheuristics.

关键词： Metaheuristic algorithm beluga whale optimization algorithm Cauchy mutation industrial system fuzzy availability engineering optimization

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A survey of beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing

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COMPUTER SCIENCE REVIEW 2025年 57卷

作者： Lee, Sang-Woong Haider, Amir Rahmani, Amir Masoud Arasteh, Bahman Gharehchopogh, Farhad Soleimanian Tang, Shengda Liu, Zhe Aurangzeb, Khursheed Hosseinzadeh, Mehdi Gachon Univ Sch Comp Pattern Recognit & Machine Learning Lab Seongnam 13120 South Korea Sejong Univ Dept AI & Robot Seoul 05006 South Korea Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Yunlin Taiwan Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Khazar Univ Dept Comp Sci Baku Azerbaijan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Guangxi Normal Univ Sch Math & Stat Guilin 541006 Peoples R China Xinyu Univ Coll Math & Comp Xinyu 338004 Peoples R China Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia King Saud Univ Coll Comp & Informat Sci Dept Comp Engn POB 51178 Riyadh 11543 Saudi Arabia Duy Tan Univ Sch Comp Sci Da Nang Vietnam Jadara Univ Res Ctr Irbid Jordan

optimization, as a fundamental pillar in engineering, computer science, economics, and many other fields, plays a decisive role in improving the performance of systems and achieving desired goals. optimization problems involve many variables, various constraints, and nonlinear objective functions. Among the challenges of complex optimization problems is the extensive search space with local optima that prevents reaching the global optimal solution. Therefore, intelligent and collective methods are needed to solve problems, such as searching for large problem spaces and identifying near-optimal solutions. Metaheuristic algorithms are a successful method for solving complex optimization problems. Usually, metaheuristic algorithms, inspired by natural and social phenomena, try to find optimal or near-optimal solutions by using random searches and intelligent explorations in the problem space. beluga whale optimization (BWO) is one of the metaheuristic algorithms for solving optimization problems that has attracted the attention of researchers in recent years. The BWO algorithm tries to optimize the search space and achieve optimal solutions by simulating the collective behavior of whales. A study and review of published articles on the BWO algorithm show that this algorithm has been used in various fields, including optimization of mathematical functions, engineering problems, and even problems related to artificial intelligence. In this article, the BWO algorithm is classified according to four categories (combination, improvement, variants, and optimization). An analysis of 151 papers shows that the BWO algorithm has the highest percentage (49%) in the improvement field. The combination, variants, and optimization fields comprise 12%, 7%, and 32%, respectively.

关键词： optimization Metaheuristic algorithms beluga whale optimization algorithm Collective behaviors

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Video enhancement for dense haze removal using an optimized multi-task evolutionary artificial neural network

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IMAGING SCIENCE JOURNAL 2025年第3期73卷 267-282页

作者： Patil, Vishalkirthi S. Havaldar, Raviraj H. Angadi Inst Technol & Management Dept Elect & Commun Engn Belagavi India KLE Technol Univ Dr M S Sheshgiri Coll Engn & Technol Dept Biomed Engn Udyambag India

Images captured during cloudy or misty weather often suffer from poor contrast, colour distortions, and limited visibility. To overcome these challenges, this manuscript proposes a Video Enhancement for Dense Haze Removal using an Optimized Multi-Task Evolutionary Artificial Neural Network (VE-MTEANN-BWOA-DHR). Initially, input videos are sourced from the Real Haze Video Database. Then Adaptive Self-Guided Filtering (ASGF) for eliminate noise from the input video. Then Ternary Pattern with Discrete Wavelet Transform (TPDWT) is used to extract the features. The extracted features are given to a Multi-Task Evolutionary Artificial Neural Network (MTEANN) to classify the dense haze levels in video frames as hazy image 1, hazy image 2, hazy image 3, and hazy image 4. Typically, MTEANN lacks adaptive optimization strategies to determine ideal parameters for effective video enhancement. Therefore, the beluga whale optimization algorithm (BWOA) is utilized to optimize MTEANN. The proposed VE-MTEANN-BWOA-DHR method demonstrates superior performance compared to the existing models.

关键词： Adaptive self-guided filtering beluga whale optimization algorithm Multi-Task Evolutionary Artificial Neural Network Ternary pattern with discrete wavelet transform video enhancement Haze Classification Dense Haze Removal Computer Vision

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Research on multiobjective capacity configuration optimization of grid-connected wind-solar-storage microgrid system based on improved BWO algorithm

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ENERGY SCIENCE & ENGINEERING 2024年第6期12卷 2365-2384页

作者： Wang, Ziheng Wang, Tao Niu, Qunfeng Wu, Jianfeng Li, Mingwei Zhu, Shuaiqi Henan Univ Technol Sch Elect Engn Zhengzhou 450001 Peoples R China Beijing Boshenkang Technol Co Ltd Beijing Peoples R China Henan Univ Technol Logist Serv Ctr Zhengzhou Peoples R China

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind-solar-storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition-based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision-makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid-connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind-solar-storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low-carbon power and regional economic development. The best-found levelized cost of energy for the wind-solar-storage microgrid system is 0.192 yuan/kWh.

关键词： beluga whale optimization algorithm capacity optimization microgrid multiobjective evolutionary algorithm renewable energy system

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Frequency Regulation in Interconnected Power System Through Enhanced beluga whale Optimized Flatness-Based Active Disturbance Rejection Control

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IEEE ACCESS 2024年 12卷 15348-15367页

作者： Ali, Shahzad Xia, Yuanqing Khan, Zohaib Ahmad Ali, Abid Navid, Qamar Aurangzeb, Khursheed Anwar, Muhammad Shahid Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh 11543 Saudi Arabia Gachon Univ Dept AI & Software Seongnam 13120 South Korea

Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced beluga whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced beluga whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustness of the dynamic response of the Flatness-based active disturbance rejection control as compared to other approaches under load variations and parameter uncertainty.

关键词： Flatness-based Active disturbance rejection control beluga whale optimization algorithm Photovoltaic generation multi-area interconnected power systems renewable sources

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Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks

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ENERGIES 2024年第24期17卷 6325-6325页

作者： Cao, Hu Ma, Lingling Liu, Guoying Liu, Zhijian Dong, Hang Kunming Power Supply Design Inst Co Ltd Kunming 650118 Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China

The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of the heavy load, low voltage, and increased network loss caused by the large number of electric vehicle (EV) charging piles and distributed photovoltaic (PV) access in urban, old and unbalanced distribution networks. At the stage of selecting the location of energy storage, a comprehensive power flow sensitivity variance (CPFSV) is defined to determine the location of the energy storage. At the energy storage capacity configuration stage, the energy storage capacity is optimized by considering the benefits of peak shaving and valley filling, energy storage costs, and distribution network voltage deviations. Finally, simulations are conducted using a modified IEEE-33-node system, and the results obtained using the improved beluga whale optimization algorithm show that the peak-to-valley difference of the system after the addition of energy storage decreased by 43.7% and 51.1% compared to the original system and the system with EV and PV resources added, respectively. The maximum CPFSV of the system decreased by 52% and 75.1%, respectively. In addition, the engineering value of this method is verified through a real-machine system with 199 nodes in a district of Kunming. Therefore, the energy storage configuration method proposed in this article can provide a reference for solving the outstanding problems caused by the large-scale access of EVs and PVs to the distribution network.

关键词： energy storage site selection and capacity determination distribution network comprehensive power flow sensitivity variance beluga whale optimization algorithm electric vehicles photovoltaic consumption

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Two-stage heuristic algorithm for vehicle-drone collaborative delivery and pickup based on medical supplies resource allocation

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第10期35卷

作者： Han, Yuhang Zhang, Miaohan Nan, Pan Cao, Jianing Huang, Qi Ye, Xiao He, Zhaolei Kunming Univ Sci & Technol Fac Civil Aviat & Aeroaut Kunming 650500 Yunnan Peoples R China Metering Ctr Yunnan Power Grid Co Ltd Kunming 650500 Yunnan Peoples R China

This paper aims to optimize medical material distribution in closed community logistics networks during sudden outbreaks with a focus on efficient waste collection and reduced consumable distribution time. First, considering the costs of UAV trajectory distribution, impact, threat, and other costs, a forward and reverse scheduling model with time windows for joint multi-distribution center material distribution and waste anti-epidemic materials collection vehicle-UAV is established. Meanwhile, a two-stage metaheuristic algorithm is proposed in this paper. In the first stage of the solution algorithm, we design the multi-strategy guided adaptive differential evolution (MSGA-DE) to plan the multi-UAV cooperative distribution situation in a 3D environment. In the second stage, an improved beluga whale optimization based on hybrid neighborhood search (HNS-IBWO) is combined to solve the vehicle-UAV scheduling and distribution problem. Furthermore, comparing with various cross-validation algorithms, it validates the superiority of MSGA-DE in solving UAV trajectory issues and the convergence speed and accuracy of HNS-IBWO for high-latitude complex optimizations. Finally, a simulation in a closed Shanghai community validates the proposed model. Results demonstrate its effectiveness, especially in terms of convergence, multi-objective search, and global search capabilities when compared to existing algorithms. This offers an efficient solution for vehicle-UAV scheduling in unforeseen epidemic-related closures.

关键词： Joint vehicle-UAV delivery Medical supplies distribution Forward and reverse logistics Hybrid neighborhood search beluga whale optimization algorithm

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Multimodal representative answer extraction in community question answering

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2023年第9期35卷

作者： Li, Ming Ma, Yating Li, Ying Bai, Yixue China Univ Petr Sch Econ & Management Beijing 102249 Peoples R China

To solve the information overload problem of multimodal answers in community question answering (CQA), this paper proposes a multimodal representative answer extraction method. First, the method of similarity calculation between multimodal answers is constructed, and multimodal clustering is used to cluster answers. Then, a binary multi-objective optimization model with three objective functions including multimodal answer coverage, multimodal answer redundancy, and multimodal answer consistency is constructed to extract a representative subset of answers. The improved beluga whale optimization algorithm (MTRL-BWO), based on tent mapping, reinforcement learning, and multiple swarm strategy, is designed to increase the diversity of the population while avoiding local optima to improve the search capability and solution accuracy of the algorithm. Experimental results show the feasibility and superior performance of the proposed method. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Community question answering Multimodality Representative answer extraction Multi-objective optimization beluga whale optimization algorithm

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Video enhancement for dense haze removal using an optimized multi-task evolutionary artificial neural network

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The Journal of Photographic Science 2025年第3期73卷

作者： Vishalkirthi S. Patil Raviraj H. Havaldar a Department of Electronics and Communication Engineering Angadi Institute of Technology and Management Belagavi India b Department of Biomedical Engineering KLE Technological University’s DR. M. S. Sheshgiri College of Engineering & Technology Udyambag India

ABSTRACTImages captured during cloudy or misty weather often suffer from poor contrast, colour distortions, and limited visibility. To overcome these challenges, this manuscript proposes a Video Enhancement for Dense Haze Removal using an Optimized Multi-Task Evolutionary Artificial Neural Network (VE-MTEANN-BWOA-DHR). Initially, input videos are sourced from the Real Haze Video Database. Then Adaptive Self-Guided Filtering (ASGF) for eliminate noise from the input video. Then Ternary Pattern with Discrete Wavelet Transform (TPDWT) is used to extract the features. The extracted features are given to a Multi-Task Evolutionary Artificial Neural Network (MTEANN) to classify the dense haze levels in video frames as hazy image 1, hazy image 2, hazy image 3, and hazy image 4. Typically, MTEANN lacks adaptive optimization strategies to determine ideal parameters for effective video enhancement. Therefore, the beluga whale optimization algorithm (BWOA) is utilized to optimize MTEANN. The proposed VE-MTEANN-BWOA-DHR method demonstrates superior performance compared to the existing models.

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