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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第1期9卷 14-30页

作者： Hosny, Khalid M. Awad, Ahmed I. Khashaba, Marwa M. Fouda, Mostafa M. Guizani, Mohsen Mohamed, Ehab R. Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Masdar City Abu Dhabi U Arab Emirates Zagazig Univ Dept Informat Technol Zagazig 44519 Egypt

Despite the extensive use of IoT and mobile devices in the different applications, their computing power, memory, and battery life are still limited. Multi-Access Edge Computing (MEC) has recently emerged to address the drawbacks of these limitations. With MEC on the network's edge, mobile and IoT devices can offload their computing operations to adjacent edge servers or remote cloud servers. However, task offloading is still a challenging research issue, and it is necessary to improve the overall Quality of Service (QoS) and attain optimized performance and resource utilization. Another crucial issue that is usually overlooked while handling this matter is offloading an application that consists of dependent tasks. In this study, we suggest a Refined whale optimization algorithm (RWOA) for solving the multiuser dependent tasks offloading problem in the Edge-Cloud computing environment with three objectives: 1- minimizing the application execution latency, 2- minimizing the energy consumption of end devices, and 3- the charging cost for used resources. We also avoid the traditional binary planning mechanisms by allowing each task to be partially processed simultaneously at three processing locations (local device, MEC, cloud). We compare RWOA with other Optimizers, and the results demonstrate that the RWOA has optimized the fitness by 52.7% relative to the second best comparison optimizer.

关键词： Task analysis Cloud computing Servers Internet of Things Energy consumption Costs whale optimization algorithms multi-access edge computing multi-objective computational offloading multi-user scenario task dependency whale optimization algorithm

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Multi-trial Vector-based whale optimization algorithm

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Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The whale optimization algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based whale optimization algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms optimization Engineering design problems whale optimization algorithm

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Improving the energy performance of vortex pump based on whale optimization algorithm

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2025年第1期19卷

作者： Zhou, Peijian Wen, Zaipeng Wang, Yang Wu, Yanzhao Wu, Denghao Huang, Renfang Yao, Zhifeng China Jiliang Univ Coll Metrol Measurement & Instrument Hangzhou Peoples R China China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Mech Key Lab Mech Fluid Solid Coupling Syst Beijing Peoples R China

This study introduced a multi-objective optimization framework for vortex pumps, utilizing the whale optimization algorithm (WOA) and Gaussian process regression (GPR) to enhance energy performance under various operating conditions. Initially, 12 design parameters were analysed using the Plackett-Burman test, identifying five critical hydraulic parameters. These parameters formed the basis for optimizing the pump's head and weighted efficiency. A surrogate model database was created using Latin hypercube sampling, and GPR facilitated the optimization process. The application of WOA resulted in a 1.94 m increase in head, a 1.72% rise in efficiency, and a 1.69% improvement in weighted efficiency. Entropy production and rigid vorticity analysis further showed a significant reduction in energy loss across pump components. This research offers a robust framework for the efficient and energy-saving design of vortex pumps.

关键词： Vortex pump optimization design whale optimization algorithm entropy production theory rigid vorticity

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Multi-Strategy Hybrid whale optimization algorithm Improvement

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APPLIED SCIENCES-BASEL 2025年第4期15卷 2224-2224页

作者： Xie, Xie Yang, Yulin Zhou, Huan Shenyang Univ Key Lab Mfg Ind & Integrated Automat Shenyang 110044 Peoples R China

The whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm developed by Mirjalili and Lewis in 2016 based on the foraging behavior of whales. Because of its simplicity and high efficiency, scholars have adopted this algorithm to address various problems in different disciplines. However, standard WOA has the problems of slow convergence speed, insufficient search accuracy, and limited ability to solve complex problems. In order to solve these problems, this paper proposes a multi-strategy hybrid whale algorithm (MHWOA). Firstly, the calculation speed is accelerated by modifying the parameters;then, the accuracy of the algorithm is improved by incorporating the scatter search strategy;finally, the simulated annealing algorithm is integrated to improve its ability to solve complex problems. The performance differences between MHWOA, the baseline algorithm, and the improved WOA algorithm are compared using the CEC2017 test suite and three real-world engineering problems. In the comparison of processing results of various problems, the calculation accuracy of MHWOA is improved by no less than 1.96%, the calculation error is reduced by no less than 1.83%, and the execution time is improved by no less than 5.6%. In the CNN-MHWOA-based time series electricity load forecasting problem, MHWOA shows the advantages of reduced error and improved fitting degree with the true value compared with the standard WOA.

关键词： whale optimization algorithm decentralized search strategy simulated annealing algorithm calculation accuracy time-consuming execution

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Routing in Wireless Sensor Networks Using Clustering Through Combining whale optimization algorithm and Genetic algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第3期38卷

作者： Zhao, Guoliang Meng, Xianmeng Yancheng Polytech Coll Sch Intelligent Mfg Yancheng Jiangsu Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Anhui Peoples R China

The development of numerous wireless sensor network (WSN) applications has sparked considerable interest in the use of these networks across various fields. These networks, which do not require infrastructure and are self-organizing, can be rapidly deployed in most locations to collect information about environmental phenomena and transmit it to relevant hubs for appropriate action in emergency situations. Sensor nodes (SNs) in WSNs function as both sensors and relay nodes in relation to one another. As energy in these networks is limited, the nodes are supplied with only a specific amount of power. Because these networks are often located in difficult and remote areas, node batteries cannot be recharged or replaced. As a result, energy conservation is one of the most pressing concerns in these networks. Consequently, this study proposes a novel optimization technique for clustering WSNs, combining the whale optimization method and the genetic algorithm. In this work, information is transferred between cluster heads (CHs) and the sink using a combination of whale optimization and evolutionary algorithms, focusing on reducing intracluster distances and energy consumption in cluster members (CMs), while achieving near-optimal routing. The implementation results demonstrate that the proposed technique outperforms previous methods in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, considering the evolutionary capabilities of both the whale optimization algorithm and the genetic algorithm.

关键词： clustering genetic algorithm whale optimization algorithm wireless sensor networks

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Neural network sliding mode control of multi-agent systems based on a preview mechanism and whale optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2025年第5期239卷 823-840页

作者： Chen, Yuxin Ren, Junchao Northeastern Univ Coll Sci 3-11 Wenhua Rd Shenyang 110819 Liaoniong Peoples R China

In this work, a neural network sliding mode control (SMC) scheme is proposed to address the tracking issue of discrete-time multi-agent systems (MASs) with unknown nonlinearities by combining the preview mechanism and whale optimization algorithm. An augmented error system (AES) was constructed, which includes previewable reference and disturbance signals. A new sliding mode surface is designed for AES, and the stability criteria are proposed for the sliding mode dynamics. Utilizing the preview mechanism and whale optimization algorithm, the neural network-based SMC law is designed to satisfy the discrete-time reachability condition. Two simulation examples are provided to demonstrate that the proposed control scheme can effectively enhance the tracking performance of MASs.

关键词： Preview mechanism sliding mode control multi-agent systems neural network whale optimization algorithm

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An improved whale optimization algorithm for multi-robot path planning

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ENGINEERING optimization 2024年第6期57卷

作者： Shao, Yijun Zhu, Liangkuan Su, Chunyu Wang, Jingyu Northeast Forestry Univ Sch Comp & Control Engn Harbin Peoples R China Northeast Forestry Univ Sch Mech & Elect Engn Harbin Peoples R China

Multi-robot path planning is challenging and has increasingly attracted attention with its widespread applications. This article proposes an improved whale optimization algorithm (WOA) with Refracted opposition-based learning and Quantum behaviour (RQWOA). The algorithm is able to plan smooth and collisionless paths for robots combining cubic spline interpolation and multi-robot coordination. A quantum behavioural mechanism is used to coordinate the evolution of the whale population during the variable phase to increase the population quality and balance the exploration and exploitation capabilities of the WOA. Simultaneously, refracted opposition-based learning is introduced to improve the algorithm's optimization accuracy and convergence speed. The RQWOA was compared with seven efficient algorithms in experiments on classical test functions and multi-robot path planning cases. The results of these methods were tested statistically. The experimental results indicate that the RQWOA has superior solution accuracy. The RQWOA is highly competitive in terms of pathlength and stability in solving multi-robot path planning problems.

关键词： whale optimization algorithm quantum behaviour refracted opposition-based learning path planning metaheuristic algorithm

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A Nonlinear Adaptive Weight-Based Mutated whale optimization algorithm and Its Application for Solving Engineering Problems

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IEEE ACCESS 2024年 12卷 40225-40254页

作者： Wang, Zhi Li, Yayun Wu, Lei Guo, Qiang SINOPEC Petr Engn Corp Dongying 257000 Peoples R China China Univ Petr Sch Petr Engn Qingdao 266580 Peoples R China

whale optimization algorithm (WOA) is a swarm-based optimization algorithm with exceptional performance and significant originality. In this study, a novel variant of WOA called nonlinear adaptive weight-based mutated WOA (NAWMWOA) is proposed to overcome the shortcomings of original WOA such as easily falling into local optimum and slow convergence speed. In detail, the proposed NAWMWOA includes three novel strategies as comparing with original WOA. Firstly, a nonlinear convergence factor is embedded into the original WOA to balance exploration and exploitation ability. The second improvement is an adaptive weight strategy, which can enhance the exploratory searching trends and improve the solution accuracy. Moreover, the thirdly proposed hybrid mutation strategy has the function of increasing the accuracy and jumping out of the local optimum. The combination of the three strategies significantly improve convergence efficiency and search accuracy of original WOA. To verify the remarkable performance of the proposed NAWMWOA, a series of illustrious WOA variants and state-of-the-art intelligent algorithms is compared with the NAWMWOA on 37 benchmark functions and three typical engineering problems. The details of experimental and statistics results illustrate that the presented NAWMWOA has higher convergence efficiency and better solution accuracy. As a conclusion, the proposed NAWMWOA is a competitive and outstanding algorithm that can effectively solve optimization problems in practical engineering.

关键词： whale optimization algorithm swarm intelligence adaptive weight engineering design

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MCWOA Scheduler:Modified Chimp-whale optimization algorithm for Task Scheduling in Cloud Computing

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Computers, Materials & Continua 2024年第2期78卷 2593-2616页

作者： Chirag Chandrashekar Pradeep Krishnadoss Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Center for Cyber Physical Systems School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as *** has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud ***,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing *** proposed Modified Chimp-whale optimization algorithm called Modified Chimp-whale optimization algorithm(MCWOA),combines elements of the Chimp optimization algorithm(COA)and the whale optimization algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution ***,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating *** study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam *** outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection *** excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage *** efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and *** simulated data indicates that the new MCWOA outpaces other methods across all *** study also references the whale optimization algorithm(WOA),Chimp algorithm(CA),Ant Lion Optimizer(ALO),Genetic algorithm(GA)and Grey Wolf Optimizer(GWO).

关键词： Cloud computing scheduling chimp optimization algorithm whale optimization algorithm

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A federated learning model with the whale optimization algorithm for renewable energy prediction

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Chifu, Viorica Rozina Cioara, Tudor Anitei, Cristian Daniel Pop, Cristina Bianca Anghel, Ionut Toderean, Liana Tech Univ Cluj Napoca Comp Sci Dept Memorandumului 28 Cluj Napoca 400114 Romania

Federated prediction models for energy prosumers create a global model by combining insights from local machine learning models trained on-site without centralizing the data. For time series energy data, this collaborative approach faces challenges due to the non-IID nature of the data, variations in generation patterns, the high number of model parameters, and convergence issues, leading to poor prediction accuracy. This paper introduces a novel federated learning model, FedWOA, which uses the whale optimization algorithm to determine optimal aggregation coefficients based on the local model weight vectors by pondering the updates considering the model performance and data dimensionality construct the global shared model. To handle the non-IID data the prosumers were clustered based on the similarity of their energy profiles using KMeans. FedWOA improves the prediction quality at the prosumer site, with a 16 % average reduction of the mean absolute error compared to FedAVG while demonstrating good convergence and reduced loss.

关键词： Federated learning whale optimization algorithm Renewable energy prediction Smart grid LSTM neural network K -Means clustering

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