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Deadline and budget-constrained archimedes optimization algorithm for workflow scheduling in cloud

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-27页

作者： Kushwaha, Shweta Singh, Ravi Shankar BHU Dept Comp Sci & Engn Indian Inst Technol Varanasi 221005 Uttar Pradesh India

Cloud computing has revolutionized various domains over the past decade, providing accessible computational and storage resources at reduced costs. The exponential growth in data volumes and processing complexity, particularly due to the proliferation of IoT devices and applications across fields such as business, education, and agriculture, requires scalable computing resources and efficient processing. Workflow scheduling in cloud computing, an NP-hard optimization problem, involves allocating resources to tasks within a workflow and determining their execution sequence. Despite numerous heuristic, metaheuristic, and hybrid approaches, there remains a need for scheduling algorithms with lesser computational complexity to optimize makespan and cost efficiency, as well as ensure SLA compliance. This paper introduces a novel multi-objective metaheuristic solution, the Deadline and Budget constrained archimedes optimization algorithm (ADB), which addresses workflow scheduling by optimizing makespan and cost while adhering to deadline and budget constraints. Extensive experiments on a well-known cloud simulation tool, Workflowsim, using scientific workflows demonstrate significant improvements in makespan (20%), cost (5%), resource utilization (15%), and energy consumption (9%). Performance observations on Pareto optimality metrics show that our approach has a higher hypervolume for 80% cases, it dominates state of the art by at least 83%, and the s-metric value of our approach is lower for 95% cases, alongside statistical validation using t-tests and ANOVA, confirming the efficacy of our method compared to state-of-the-art approaches.

关键词： optimization techniques Workflow scheduling Metaheuristics Pareto-optimality Deadline Budget Cost-efficiency archimedes optimization algorithm

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An enhanced multi-objective reactive power dispatch for hybrid Wind-Solar power system using archimedes optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 168卷

作者： Megantoro, Prisma Abd Halim, Syahirah Kamari, Nor Azwan Mohamed Awalin, Lilik Jamilatul Mohamed, Ramizi Rosli, Hazwani Mohd Univ Kebangsaan Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi Malaysia Univ Airlangga Fac Adv Technol & Multidiscipline Kampus C UnairJl Mulyorejo Surabaya 60155 Indonesia Asia Pacific Univ Technol & Innovat Sch Engn Kuala Lumpur Malaysia

Optimal reactive power dispatch (ORPD) is essential for addressing power system challenges related to distributed generation (DG), particularly from renewable energy (RE) sources such as wind and solar. The intermittent nature and uncertainty of these energy sources, influenced by varying wind speeds and solar irradiation, complicate their integration into power systems. This paper proposes a solution to the ORPD problem in systems with RE-DG integration using the archimedes optimization algorithm (AOA). The uncertainties of wind and solar power generation were modelled using Weibull and lognormal probability density functions (PDFs), respectively, and the optimization model was tested using a scenario-based method. The AOA was applied to the IEEE 57 bus system to minimize power loss, voltage deviation, and voltage stability index (VSI). The results demonstrated that AOA contributed to a 15.7% reduction in power loss, and an 83.9% enhancement in VSI compared to the base case. In the multi-objective optimization scenario, AOA achieved a 7.1% reduction in power loss, with an additional 11.6% improvement upon the integration of DGs. The performance of AOA was also compared with other metaheuristic algorithms, demonstrating superior results in terms of tracking accuracy and convergence speed. AOA outperformed the multi-objective ant lion optimization (MOALO) and the Levy-based Interior Search algorithm (LISA) in terms of power loss reduction and voltage stability. AOA achieved a 1.83% lower power loss and a 29.67% lower VSI compared to MOALO. When compared to LISA, AOA achieved a 1.68% lower power loss, demonstrating its superior optimization capabilities. These findings confirm that AOA is a highly effective method for solving the ORPD problem, accounting for renewable energy uncertainties and improving overall system performance.

关键词： archimedes optimization algorithm Distribution generation Metaheuristics optimization Optimal reactive power dispatch Voltage stability index

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Multi-Objective Workflow Scheduling in Cloud Using archimedes optimization algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第4-5期37卷

作者： Kushwaha, Shweta Singh, Ravi Shankar Prajapati, Kanika Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi India

Cloud computing has changed the technology landscape for over a decade and led to an astounding growth in the number of applications it may be used for. Consequently, there has been a significant spike in the demand for improved algorithms to schedule workflows efficiently. These were mostly concerned with heuristic, metaheuristic, and hybrid approaches to workflow scheduling that mostly suffer from the problem of local optima entrapment. Due to such heavy traffic on the cloud resources, there is still a need for less computationally complex approaches. In light of this, this article proposes a novel approach: a multi-objective Modified Local Escaping archimedes optimization (MLEAO) algorithm for workflow scheduling. This strategy involves initialization of the population of archimedes optimization algorithm through the HEFT algorithm to provide an inclination towards the solutions with improved makespan while achieving a cost-efficient workflow scheduling decision and avoiding the problem of local optima entrapment using a local escaping operation. To validate the efficacy of our approach, we conducted extensive experiments using scientific workflows as benchmarks. Through our investigations, we significantly improved makespan, cost, resource utilization, and energy consumption. Moreover, the effectiveness of our proposed approach is also verified by performance metrics such as hypervolume, s-metric, and dominance relationships between the proposed and state-of-the-art approaches.

关键词： archimedes optimization algorithm cost-efficiency local escaping operation metaheuristics optimization techniques pareto-optimality workflow scheduling

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archimedes optimization algorithm With Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector

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IEEE ACCESS 2024年 12卷 29768-29777页

作者： Issaoui, Imene Alohali, Manal Abdullah Mtouaa, Wafa Alotaibi, Faiz Abdullah Mahmud, Ahmed Assiri, Mohammed Qassim Univ Appl Coll Unit Sci Res Buraydah 52571 Saudi Arabia Univ Sfax MIRACL Lab FSEG Sfax 3029 Tunisia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Arts Dept Math Muhayil 62529 Saudi Arabia King Saud Univ Coll Humanities & Social Sci Dept Informat Sci POB 28095 Riyadh 11437 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Aflaj Dept Comp Sci Al Kharj 16273 Saudi Arabia

Content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has significant applications in healthcare, including medical diagnosis, research, and treatment planning. It is a diagnostic tool that enhances the explainability of computer-aided diagnoses (CAD) systems and provides decision-making support for healthcare professionals. A conventional way to CBIR is learning a distance metric by converting images into feature space where the distance between samples is a similarity measurement. CBIR systems can autonomously extract intricate features from medical images by leveraging convolutional neural networks (CNN) and other advanced deep learning techniques, enabling accurate and swift retrieval of relevant patient cases and diagnostic references. This manuscript designs an archimedes optimization algorithm with Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector (AOADL-CBIRH) technique. The AOADL-CBIRH technique intends to retrieve similar images based on query images in the healthcare sector. In the presented AOADL-CBIRH technique, image pre-processing is initially performed using an adaptive bilateral filtering (ABF) approach to enhance the image quality. For deep feature extraction, the AOADL-CBIRH method applies the Efficient Channel Spatial Model (ECSM) with the ResNet50 model. To improve the retrieval performance, the AOADL-CBIRH technique employs AOA-based hyperparameter tuning for the ECSM-ResNet50 model. Lastly, the Manhattan distance metric determines the similarity between the images and retrieves them. The experimental evaluation of the AOADL-CBIRH algorithm is tested by using benchmark image dataset. The stimulation values signified the enhanced retrieval results of the AOADL-CBIRH technique over other models.

关键词： Feature extraction Medical services Image retrieval Residual neural networks Tuning Medical diagnostic imaging Solid modeling Content management Deep learning optimization methods Content-based image retrieval healthcare similarity measurement deep learning archimedes optimization algorithm

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archimedes optimization algorithm based approaches for solving energy demand estimation problem: a case study of Turkey

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NEURAL COMPUTING & APPLICATIONS 2023年第26期35卷 19627-19649页

作者： Aslan, Murat Sirnak Univ Fac Engn Dept Comp Engn TR-73000 Sirnak Turkiye

Energy consumption is getting rising gradually around the planet. Therefore, the importance of energy management has increased for all nations worldwide, and long-term energy demand estimation is becoming a vital problem for all countries. In this study, linear, quadratic and exponential models based six different archimedes optimization algorithms (AOA) such as AOA-Linear, AOA-Quadratic, AOA-Exponential, IAOA-Linear, IAOA-Quadratic and IAOA-Exponential have been proposed to make some future projections of Turkey for the years (2021-2050). The previous studies in the literature were used the data set of Turkey, such as observed energy demand (OED), population, gross domestic product (GDP), export and import data for the years (1979-2005) or (1979-2011) obtained from the Turkish Statistical Institute (TUIK) and the Ministry of Energy and Natural Resources (MENR). However, in this study, a new data set is organized with the OED, population, GDP, export and import data of Turkey for the years (1997-2020) to make some long-term energy demand estimations of Turkey, and this dataset is used for the first time in this study. AOA-Linear, AOA-Quadratic and AOA-Exponential algorithms are based on linear, quadratic and exponential mathematical models and the basic AOA method. IAOA-Linear, IAOA-Quadratic and IAOA-Exponential algorithms are also based on linear, quadratic and exponential mathematical models and the improved AOA (For short, IAOA) proposed in this study. Once a sensitivity analysis is made for determining the effect of algorithmic parameters of AOA and IAOA, the proposed algorithms are realized for Turkey's long-term energy demand estimation for the years (2021-2050) with three different future scenarios. According to the experimental results, the quadratic model-based proposed IAOA produces better or comparable performance on the problem dealt with in this study in terms of solution quality and robustness.

关键词： archimedes optimization algorithm Energy forecasting Linear regression model Long-term energy demand Quadratic model

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A quantum-inspired archimedes optimization algorithm for hybrid-load autonomous guided vehicle scheduling problem

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APPLIED INTELLIGENCE 2023年第22期53卷 27725-27778页

作者： Zhou, Binghai Zhao, Lingwei Tongji Univ Inst Ind Engn Sch Mech Engn Caoan Rd 4800Mech Bldg A444 Shanghai 201804 Peoples R China

Facing the two major challenges of just-in-time (JIT) and energy-saving in part-feeding system for mixed-model assembly lines in automotive industry, this paper focuses on optimizing the part-feeding process by investigating the autonomous guided vehicles (AGV) routing and scheduling problem. A hybrid feeding policy, called dual-distribution, is proposed, which considers the utilization of different types of AGVs in various part-feeding policies. The problem is formulated as a mixed-integer programming model with the objectives of simultaneously minimizing the line-side inventory and AGV energy consumption in the part-feeding system. To solve this problem, a Q-learning-based multi-objective quantum-inspired archimedes optimization algorithm (QMQAOA) is developed with a customized encoding and decoding approach to solve the problem. Besides, the quantum rotation gate initialization mechanism, the Q-learning-based neighborhood search strategy, and the neighboring distance calculation are integrated into the algorithm to improve both solution quality and convergence rate. Finally, numerical experiments are conducted to evaluate the performance of QMQAOA by comparing it with the Gurobi solver and other benchmark algorithms. The results demonstrate the superiority of QMQAOA, with performance superiority rates of 90/90, 77/90, and 90/90 achieved for degree of Pareto optimality (DPO), evenness of solutions (ES), and inverted generational distance (IGD) indicators, respectively. From managerial insights, the application of different types of AGVs in the part-feeding system is shown to enhance efficiency when compared to using a single type of AGV in several instances. These findings provide valuable insights for optimizing the automotive part-feeding system.

关键词： Q-learning Quantum-inspired algorithm archimedes optimization algorithm Hybrid part-feeding policy AGV scheduling

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An Optimal Microgrid Operations Planning Using Improved archimedes optimization algorithm

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IEEE ACCESS 2022年 10卷 67940-67957页

作者： Trong-The Nguyen Dao, Thi-Kien Thi-Thanh-Tan Nguyen Trinh-Dong Nguyen Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Elect Power Univ Informat Technol Fac Hanoi 100000 Vietnam Univ Informat Technol Ho Chi Minh City 700000 Vietnam Vietnam Natl Univ Ho Chi Minh City 700000 Vietnam

More new energy sources have been incorporated into a microgrid model with parameter space growing exponentially, causing optimization scheduling as a nonlinear issue to become more complex and difficult to calculate. This study suggests an improved archimedes optimization algorithm (IAOA) increases optimal performance for the microgrid operations planning issue. A multiobjective function about optimization planning issues is constructed with relevant economic costs and environmental profits for a microgrid community system (MCS). The IAOA is implemented based on the archimedes optimization algorithm (AOA) by adding reverse learning and multi-directing strategies to avoid the local optimum trap when dealing with complicated situations. The experimental results of the suggested approach on the CEC2017 test suite and microgrid operations planning problem are compared to the various algorithms in the identical condition scenarios to evaluate the recommended approach performance. Compared findings reveal that the suggested IAOA outperforms the various algorithms in comparison, practical solution, and high feasibility.

关键词： Microgrids Planning optimization Power generation Costs Generators Wind turbines Microgrid operations planning archimedes optimization algorithm microgrid community system improved archimedes optimization algorithm

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EAOA: An Enhanced archimedes optimization algorithm for Feature Selection in Classification

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IEEE ACCESS 2021年 9卷 120795-120814页

作者： Desuky, Abeer S. Hussain, Sadiq Kausar, Samina Islam, Md Akhtarul El Bakrawy, Lamiaa M. Al Azhar Univ Fac Sci Cairo 11754 Egypt Dibrugarh Univ Examinat Branch Dibrugarh 786004 Assam India Univ Kotli Azad Jammu & Kashmir Dept Comp Sci & IT Kotli Azad Kashmir 11100 Pakistan Khulna Univ Stat Discipline Sci Engn & Technol Sch Khulna 9208 Bangladesh

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is archimedes optimization algorithm (AOA). This paper proposes an Enhanced archimedes optimization algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

关键词： archimedes optimization algorithm classification feature selection machine learning metaheuristic algorithms optimization algorithms

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Impact of fast charging station for electric vehicles with grid integration: Forensic-based investigation and archimedes optimization algorithm approach

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第3期45卷 1305-1326页

作者： Singh, Abhishek Kumar Kumar, Ashwani Natl Inst Technol Dept Elect Engn Kurukshetra India

This manuscript proposes a novel technique for the precise model of electric vehicles (EVs) in the reliability and adequacy model of smart grids (SG). The proposed method combines forensic-based investigation (FBI) and archimedes optimization algorithm (AOA), named the FBIAOA technique. The objective of the proposed method is to rise the profit of fast charging stations and lessen the rising energy demand on the grid that is made up of storage systems and renewable energy generation (wind and PV). The demand for EVs and renewable generation is calculated using the FBI algorithm method. The growth of the proposed method is to examine the reliability of SG depending on the aggregation of the state matrices of EV stochastic parameters. The proposed method can help accelerate the reliability calculations by determining the desired count of EV states. The proposed strategy is run in MATLAB and is evaluated in its performance with existing methods. The proposed method gives a lower cost than the existing genetic algorithm, cuttlefish algorithm, and tunicate swarm algorithm methods. This manuscript introduces FBIAOA, a novel method combining forensic-based investigation (FBI) and archimedes optimization algorithm (AOA) for precise electric vehicle (EV) modeling in smart grid (SG) reliability studies. The approach aims to maximize fast charging station (FCS) profits, reduce grid energy demand using storage and renewable sources, and assess SG reliability through aggregated stochastic EV parameters. Implemented in MATLAB, the proposed FBIAOA method demonstrates cost-effectiveness compared to existing techniques. image

关键词： archimedes optimization algorithm battery demand electric vehicle forensic-based investigation algorithm grid integration photovoltaic wind turbine

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A hierarchical chain-based archimedes optimization algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第12期20卷 20881-20913页

作者： Zhang, Zijiao Wu, Chong Qu, Shiyou Liu, Jiaming Harbin Inst Technol Sch Management Harbin 150000 Peoples R China Beijing Technol & Business Univ Sch Comp Sci & Artificial Intelligence Beijing 100048 Peoples R China

The archimedes optimization algorithm (AOA) has attracted much attention for its few parameters and competitive optimization effects. However, all agents in the canonical AOA are treated in the same way, resulting in slow convergence and local optima. To solve these problems, an improved hierarchical chain-based AOA (HCAOA) is proposed in this paper. The idea of HCAOA is to deal with individuals at different levels in different ways. The optimal individual is processed by an orthogonal learning mechanism based on refraction opposition to fully learn the information on all dimensions, effectively avoiding local optima. Superior individuals are handled by an archimedes spiral mechanism based on Levy flight, avoiding clueless random mining and improving optimization speed. For general individuals, the conventional AOA is applied to maximize its inherent exploration and exploitation abilities. Moreover, a multi-strategy boundary processing mechanism is introduced to improve population diversity. Experimental outcomes on CEC 2017 test suite show that HCAOA outperforms AOA and other advanced competitors. The competitive optimization results achieved by HCAOA on four engineering design problems also demonstrate its ability to solve practical problems.

关键词： archimedes optimization algorithm hierarchical chain orthogonal learning Levy flight refraction opposition-based learning

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