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Cloudlet Based Computing optimization Using Variable-Length whale optimization and Differential Evolution

IEEE ACCESS 2023年 11卷 45098-45112页

作者： Muwafaq, Layth Noordin, Nor K. K. Othman, Mohamed Ismail, Alyani Hashim, Fazirulhisyam Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia Fac Engn Res Ctr Excellence Wireless & Photon Network WiPNE Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia UPM Dept Commun Technol & Networks Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia UPM Inst Math Res INSPEM Lab Computat Sci & Math Phys Serdang 43400 Selangor D E Malaysia

Cloudlet-based optimization involves deploying a set of cloudlets in an environment and assigning user tasks to optimize various metrics, including energy consumption, quality of service (QoS), and cost. Typically, approaches deal with them separately, which might cause sub-optimality. Furthermore, assuming the fixed location of the cloudlets will limit the dynamic adaptability of the problem. Enabling more optimality and adaptability to the dynamic nature of cloudlet-based computing, we propose a novel Variable-Length multi-objective whale optimization Integrated with Differential Evolution designated as VL-WIDE. Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. Furthermore, it enables a non-dominated evaluation of solutions based on four objectives using crowding distance for selection. It provides an application-oriented solutions repair operator for repairing non-valid solutions and assuring that all solutions are generated in the feasible region. The proposed algorithm enables moving the cloudlets among pre-defined locations to increase the quality of service according to the change in the user density caused by user mobility. Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. VL-WIDE has provided the best in a number of non-dominated solutions and delta metrics and was competitive in other metrics.

关键词： optimization Edge computing Cloud computing whale optimization algorithms Costs Measurement Heuristic algorithms Mobile edge computing environment cloudlet deployment task offloading multi-objective optimization variable-length optimization

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Color Image Edge Detection Method Based on the Improved whale optimization Algorithm

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IEEE ACCESS 2023年 11卷 5981-5989页

作者： Liu, Dujin Zhou, Shiji Shen, Rong Luo, Xuegang Sichuan Univ Arts & Sci Sch Intelligent Mfg Dazhou 635000 Peoples R China Panzhihua Univ Sch Math & Comp Panzhihua 617000 Peoples R China

A high level of computation is required for edge detection in color images captured by unmanned aerial vehicles (UAVs) to address issues, such as noise, distortion, and information loss. Thus, an edge detection method for UAV-captured color images based on the improved whale optimization algorithm (WOA) is proposed in this study. In this method, the color image pixels are represented by quaternions, and the global random position variables and information exchange mechanism are introduced into the random walk foraging formula of the WOA. Further, a random disturbance factor is also introduced into the predator-prey formula of the spiral bubble net. The proposed improved WOA is then used to obtain the preliminary edge of the UAV-captured color image. An edge-point classification method using the radius of the shortest distance between the whale and the current global optimum in each iteration is presented to enhance a preliminary edge. The experimental results show that the proposed edge detection method has the advantages of strong denoising, fast speed, and good quality.

关键词： Image edge detection Autonomous aerial vehicles Quaternions whale optimization algorithms Image color analysis Spirals Classification and purification edge detection quaternion unmanned aerial vehicle (UAV) whale optimization algorithm (WOA)

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Fuzzy Automatic Disturbance Rejection Control of Quadrotor UAV Based on Improved whale optimization Algorithm

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IEEE ACCESS 2023年 11卷 69117-69130页

作者： Shan, Zebiao Wang, Yuhang Liu, Xiaosong Wei, Changbin Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China Changchun Meteorol Instrument Res Inst Changchun 130102 Peoples R China

With the aim of improving the rapidity and accuracy of quadrotor UAV positional trajectory tracking, this paper proposes a fuzzy automatic disturbance rejection control of quadrotor UAV based on improved whale optimization algorithm (IWOA-Fuzzy-ADRC). (1) To address the problems of low accuracy and slow convergence of the traditional whale algorithm, a combination of logistic chaos mapping and skew tent mapping is applied to improve the diversity of the initial population. (2) The global search capability is strengthened to avoid falling into local optimum through introducing the cross operator and Gaussian variational operators. (3) The IWOA is employed to iteratively optimize the adjustment coefficients of the fuzzy active disturbance rejection controller, the calculus gain of the nonlinear state error feedback control (NLSEF), and the error correction coefficients of the expansive state observer (ESO). The simulation results demonstrate that compared with the traditional ADRC controller and PID controller, the proposed IWOA-Fuzzy-ADRC controller can effectively improve the control performance of the system, accurately track the desired flight trajectory, accelerate the dynamic response of the control system, reduce the steady-state error, and enhance the anti-interference capability.

关键词： Quadrotors Control systems Heuristic algorithms Observers Autonomous aerial vehicles whale optimization algorithms Rotors Fuzzy systems Chaotic communication INDEX TERMS Gaussian processes UAV trajectory tracking fuzzy ADRC whale optimization algorithm chaotic mapping cross-operator Gaussian variant operators

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Hybridizing whale optimization Algorithm With Particle Swarm optimization for Scheduling a Dual-Command Storage/Retrieval Machine

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IEEE ACCESS 2023年 11卷 21264-21282页

作者： Hsu, Hsien-Pin Wang, Chia-Nan Natl Kaohsiung Univ Sci & Technol Dept Supply Chain Management Kaohsiung 81157 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Ind Engn & Management Kaohsiung 807618 Taiwan

whale optimization algorithm (WOA) and particle swarm optimization (PSO) have been used individually usually. However, a separate use of them has a limitation. Hybridizing WOA with PSO is expected to evolve solutions better due to the cooperation between whales and seabirds. Developing such kind of model is the focus of this research. A framework has been further proposed to best utilize such hybridizations for developing simulation-based optimization approaches. The framework has the advantage of integrating metaheuristic, simulation, and optimization seamlessly. It can waive the rigorous and labor-intensive optimization procedure required for traditional simulation. In this research, simulation-based optimization approaches are used to deal with the dual-command block scheduling problem of a manufacturing firm's storage/retrieval (S/R) machine in an automated storage/retrieval system. The S/R machine is mainly used to store/retrieve stock-keeping units in an automated storage/retrieval system. Three simulation-based optimization approaches, Hybrid1 (WOA+PSO), Hybrid2 (WOA+PSO), and Hybrid3 (WOA+PSO), have been developed. To investigate their effectiveness, experiments have been conducted to compare them with their base models, WOA and PSO, as well as the genetic algorithm (GA) and PWOA. The PWOA is an abbreviation of a hybridization of PSO and WOA proposed in a previous study. The experimental results show that Hybrid3 (WOA+PSO) outperforms Hybrid2 (WOA+PSO), Hybrid1 (WOA+PSO), WOA, PSO, PWOA, and GA. The uses of techniques such as hybridization, Neighborhood heuristic, and adaptive movements of whales empower Hybrid3 (WOA+PSO) the most.

关键词： Metaheuristics Manufacturing Genetic algorithms Job shop scheduling Analytical models Particle swarm optimization whale optimization algorithms whale optimization algorithm particle swarm optimization storage retrieval machine scheduling

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A Hybrid Heuristic Algorithm for Optimizing the Optimal Landing Time Window Problem in Intelligent Air Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Lu, Yong Chen, Shikang Pan, Xiuqin Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

The Aircraft Landing Problem (ALP) involves optimizing the scheduling of flight arrivals and departures while simultaneously managing airport resources to maximize flight utilization, minimize delays, reduce costs, and enhance overall operational efficiency. However, existing algorithms for solving ALP often face challenges in converging optimally and effectively handling penalty values associated with timing violations. To address these issues, this paper proposes a novel hybrid heuristic algorithm, AGWOA, designed to enhance convergence rates and effectively manage penalties in ALP. AGWOA integrates Artificial Bee Colony (ABC) and Genetic Algorithm (GA) techniques into the whale optimization Algorithm (WOA), leveraging their complementary strengths to strengthen global search efficiency and refine local constraint handling. This integration accelerates convergence and significantly mitigates penalty costs. Moreover, AGWOA incorporates an adaptive coefficient to facilitate improved convergence, along with a Fast Convergence Update Mechanism (FCUM) to guide the algorithm toward optimal solutions more efficiently. Experimental results conducted on public datasets demonstrate that AGWOA outperforms existing advanced algorithms in both convergence speed and penalty minimization. Specifically, AGWOA achieves a 4.05% decrease in penalty costs compared to baseline algorithms. These results underscore AGWOA's effectiveness in overcoming the challenge of slow convergence and its competitive advantage over other methods in optimizing ALP. The proposed algorithm offers a promising solution for real-world ALP applications, significantly enhancing airline operational efficiency and optimizing resource management.

关键词： Heuristic algorithms Aircraft Convergence Mathematical models Airports whale optimization algorithms Genetic algorithms Spirals whales Resource management ALP WOA heuristic algorithm problem optimization intelligent air transportation systems

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RLWOA-SOFL: A New Learning Model-Based Reinforcement Swarm Intelligence and Self-Organizing Deep Fuzzy Rules for fMRI Pain Decoding

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第2期15卷 644-656页

作者： Anter, Ahmed M. Zhang, Zhiguo Egypt Japan Univ Sci & Technol E JUST Alexandria 21934 Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Benisuef 62511 Egypt Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Peng Cheng Lab Shenzhen 518060 Peoples R China

Pain is highly subjective, so it is always desirable to develop objective pain assessment methods. Brain imaging techniques, such as functional magnetic resonance imaging (fMRI), have the potential to provide a physiological and quantitative pain assessment tool. However, the ultra-high-dimensional fMRI data and the nonlinear relationship between fMRI and pain greatly degrade the efficiency of fMRI-based pain decoding models. In this article, a novel pain decoding model is proposed based on the whale optimization algorithm (WOA), reinforcement learning (RL), and self-organizing fuzzy logic (SOFL), namely RLWOA-SOFL. The new non-linear WOA method incorporates RL and repository experiences (RE), which is based on a back-propagation neural network (BPNN) to map a set of agents states to appropriate actions, to extract and select features that are highly predictive of pain. More specifically, the proposed RLWOA is self-learning and self-optimizing so it can deal with the high-dimensional and complex fMRI data. On the other hand, to establish a fMRI-based pain decoding model, a novel SOFL method is proposed as a new type of deep fuzzy rule that can learn continuously from new data and identify prototypes to construct fuzzy rules. The proposed RLWOA-SOFL model is applied to real-world pain-evoked fMRI data, and the results show that the new model can decode pain intensity more accurately and can identify pain-related fMRI patterns more reliably. Therefore, the proposed RLWOA-SOFL model has great potential to evaluate the intensity of pain perception in clinical uses.

关键词： Pain Functional magnetic resonance imaging Decoding whales Feature extraction Brain modeling whale optimization algorithms Reinforcement learning swarm intelligence self-organizing deep fuzzy rules functional magnetic resonance imaging and pain decoding

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Rapid Prediction of Cutterhead Torque in Hard-Rock Tunneling Using IEWOA-TSVD-ITELM

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IEEE ACCESS 2024年 12卷 88658-88680页

作者： Li, Long Liu, Zaobao Shandong Technol & Business Univ Sch Management Sci & Engn Yantai 264005 Shandong Peoples R China Northeastern Univ Coll Resources & Civil Engn Key Lab Minist Educ Safe Min Deep Met Mines Shenyang 110819 Peoples R China Northeastern Univ Key Lab Liaoning Prov Deep Engn & Intelligent Tech Shenyang 110819 Peoples R China

Predicting cutterhead torque is essential for optimizing TBM construction strategies and minimizing jamming risks. This study presents a novel hybrid model (IEWOA-TSVD-ITELM), developed using data from 7635 tunneling cycles from the Yinsong Project, to enhance cutterhead torque prediction accuracy. The EWOA enhances its global search capability by introducing new position updating and adaptive adjustment strategies (IEWOA). In addition, by leveraging the Softsign function for the nonlinear transformation of the expected output matrix of the TELM, a third hidden layer is added to enhance the feature extraction capabilities (ITELM), whereas truncated singular value decomposition (TSVD) is employed to reduce the noise in the output matrix of the third hidden layer of the ITELM (TSVD-ITELM). Furthermore, the IEWOA optimized the number of neurons and randomly generated weights and biases in the TSVD-ITELM. This study comprehensively evaluates and compares six optimization algorithms using 25 standard test functions. Additionally, the IEWOA-TSVD-ITELM is compared with eight classical machine learning models. This study examines the impact of different timing lengths of the rising phase and rock mass grades on model performance. The results demonstrate the outstanding performance of the IEWOA as an optimization algorithm. The IEWOA-TSVD-ITELM model achieves an R-2 value of 0.644 on the test set, with an MAE of 326.623 and an RMSE of 435.821, outperforming the other algorithms. Increasing the timing length from 30 to 60 s reduces the MAE and RMSE by 11.82% and 9.56%, respectively, but the gains diminish when the timing length increases from 60 to 90 s.

关键词： Torque Prediction algorithms Predictive models Tunneling Rocks optimization Adaptation models whale optimization algorithms Extreme learning machines Machine learning TBM cutterhead torque prediction whale optimization algorithm truncated singular value decomposition two-hidden-layer extreme learning machine extreme hybrid model

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A Novel Computational Intelligence-Based Parameter Extraction of UAV-Integrated Photovoltaic System

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IEEE ACCESS 2024年 12卷 97664-97677页

作者： Saeedinia, Mohammadhosein Hashemi, Behzad Cretu, Ana-Maria Taheri, Shamsodin Univ Quebec Outaouais Dept Comp Sci & Engn Gatineau PQ J8X 3X7 Canada

The precise modeling of unmanned aerial vehicle (UAV)-integrated photovoltaic (PV) systems is central for their implementation on UAVs, especially due to their continuous movement. Accurate modeling is essential for developing control methods, conducting performance studies, and designing the whole system considering PV system parameters. This original study presents a novel modeling procedure for comprehensive modeling of UAV-integrated PV modules using multi-input multi-output (MIMO) and multi-input single-output (MISO) machine learning models. These models are developed based on a historical environmental dataset and a randomly generated dataset representing flight conditions. Both datasets get processed during the preparation phase. In this phase, two sets of empirically derived group of modified equations (GME) of the PV module are utilized. In addition, the whale optimization Algorithm (WOA) is employed to optimize one set of GME containing PV system five unknown parameters. Moreover, the Dogleg Trust Region Algorithm (DTRA) is used as an unconstrained problem solver in each loop of WOA to solve a system of equations consisting of the unknown parameters. Finally, the MIMO and MISO models will be trained with the processed dataset. The proposed approach predicts UAV-integrated PV module behavior under diverse flight conditions, interprets panel movements, and is validated through experiments.

关键词： Mathematical models Autonomous aerial vehicles Computational modeling Prediction algorithms optimization Predictive models Photovoltaic systems Machine learning whale optimization algorithms UAV-integrated photovoltaic machine learning moving PV UAV flying PV whale optimization algorithm (WOA)

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A Novel Renewable Power Generation Prediction Through Enhanced Artificial Orcas Assisted Ensemble Dilated Deep Learning Network

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IEEE ACCESS 2024年 12卷 44207-44223页

作者： Che, Zhifeng Amirthasaravanan, A. Al-Razgan, Muna Awwad, Emad Mahrous Mohamed, Mohamed Yasin Noor Tyagi, Vaibhav Bhushan Xinxiang Vocat & Tech Coll Xinxiang 453000 Henan Peoples R China SRM Inst Sci & Technol Dept Comp Technol Kattankulathur 603203 Tamil Nadu India King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11345 Saudi Arabia King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Sultan Qaboos Univ Dept Math & Informat Technol Muscat 123 Oman SIMATS Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 600077 Tamil Nadu India ISBAT Univ Fac Engn Kampala Uganda

The different energy resource generation tends to have high-level variation, making the power supply complex for the end-users. Because of the intermittent nature, the variations occur by time, weather conditions, and output energy. Hence, this research aims to develop a new "Renewable Power Generation Prediction (RPGP)" model using Deep Learning (DL) to give the end user a reliable power supply. The data aggregation process initially accumulated the data in a normalized and structured format. Then, the data cleaning and scaling are performed to decrease the outliers and varying ranges of values. A higher-order statistical feature was attained from the cleaned and scaled data. This statistical feature was given to "Optimal Weight Computation Ensemble Dilated Deep Network (OWC-EDDNet)" to predict generated power. In this EDDLNet, networks such as "Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN)" are employed to predict the renewable generated power. Finally, the prediction score attained from all deep networks is multiplied by the optimized weight to get the final prediction outcome, where the weights are optimally determined with the support of the Enhanced Artificial Orcas Algorithm (EAOA). The extensive empirical results were analyzed among traditional algorithms and prediction models to showcase the efficacy of the designed energy generation prediction scheme.

关键词： Renewable energy sources Predictive models whale optimization algorithms Higher order statistics Power supplies Power generation planning Long short term memory Recurrent neural networks Meteorology Energy resources Energy management Weight measurement Data aggregation Artificial neural networks Renewable power generation prediction enhanced artificial orcas algorithm higher order statistical features optimal weight computation ensemble dilated deep network

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Multi-Step-Ahead Wind Speed Forecast System: Hybrid Multivariate Decomposition and Feature Selection-Based Gated Additive Tree Ensemble Model

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IEEE ACCESS 2024年 12卷 58750-58777页

作者： Joseph, Lionel P. Deo, Ravinesh C. Casillas-Perez, David Prasad, Ramendra Raj, Nawin Salcedo-Sanz, Sancho Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Ctr Appl Climate Sci Toowoomba Qld 4350 Australia Univ Rey Juan Carlos Dept Signal Proc & Commun Madrid 28942 Spain Univ Fiji Sch Sci & Technol Dept Sci Lautoka Fiji Univ Alcala Dept Signal Proc & Commun Madrid 28805 Spain

Wind, being a clean and sustainable resource, boasts environmental advantages. However, its electricity generation faces challenges due to unpredictable variations in wind speed (WS). Accurate predictions of these variations would allow mixed grids to adjust their energy mix in real-time, ensuring overall stability. For this purpose, the paper develops a new hybrid gated additive tree ensemble (H-GATE) model for accurate multi-step-ahead WS predictions. First, the multivariate empirical mode decomposition (MEMD) simultaneously demarcates the multivariate data into intrinsic mode functions (IMFs) and residuals. These components represent underlying trends, periodicity, and stochastic patterns in WS variations. The IMF and residual components are pooled in respective sets, and an opposition-based whale optimization algorithm (OBWOA) is applied for dimensionality reduction. The selected features are used by GATE tuned with Bayesian optimization (BO) to forecast the individual IMF and residual components. The outputs are summed to obtain the final multi-step-ahead WS forecasts. The proposed H-GATE is benchmarked against standalone (S-GATE, S-CLSTM, and S-ABR) and hybrid (H-CLSTM and H-ABR) models. Based on all statistical metrics and diagnostic plots, H-GATE outperforms all comparative models at all forecast horizons, accumulating the lowest mean absolute percentage error (MAPE) of 6.13 - 9.93% (at t(L+1) ), 8.67 - 14.07% (at t(L+2 )), and 11.60 - 18.37% (at t(L+3) ) across all three sites. This novel multi-step-ahead WS forecasting strategy can significantly benefit grid operators by helping anticipate fluctuations in wind power generation. This can assist in optimizing energy dispatch schedules, reducing reliance on backup power sources, and enhancing overall grid stability. Practical implementation of this method can help meet the rising energy demands through renewable wind energy.

关键词： Forecasting Long short term memory Predictive models Logic gates Computational modeling Autoregressive processes Wind power generation Velocity measurement Wind speed whale optimization algorithms Bayes methods Wind speed forecasting gated additive tree ensemble multivariate empirical mode decomposition opposition-based whale optimization algorithm Bayesian optimization

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