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A metaheuristic for fast machining error compensation

JOURNAL OF INTELLIGENT MANUFACTURING 2016年第6期27卷 1209-1220页

作者： Stryczek, Roman Univ Bielsko Biala Dept Mfg Technol & Automat Ul Willowa 2 PL-43309 Bielsko Biala Poland

In case of complex parts machining or multi-directional machining in multi-part fixtures the error compensation in multi-dimensional decision space poses a difficult problem. The article focuses on the limitation of defective products by means of systematic increase of the remaining error budget due to correction of the setup data. A vectorial equation for machine tool space description is presented. The development of geometric dimensioning and tolerancing scheme to the levels connected with the setup data is proposed. The optimization algorithm used here is based on the paradigm particle swarm optimization (PSO), but it includes a few significant modifications inspired by the growth of the coral reef thus the name of the method-coral reefs inspired particle swarm optimization (CRIPSO). CRIPSO has been compared with three other popular metaheuristics: classic PSO, genetic algorithm, and cuckoo optimization algorithm. There is a practical example in this article.

关键词： Workpiece accuracy Error budget Machining error compensation metaheuristic optimization algorithm

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

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NEURAL COMPUTING & APPLICATIONS 2022年第8期34卷 6307-6329页

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Charmo Univ Coll Med & Appl Sci Appl Comp Dept Sulaimany Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq Sulaimani Polytech Univ Tech Coll Informat Dept Informat Technol Sulaimany Iraq Univ Human Dev Coll Sci & Technol Dept Informat Technol Sulaimany Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

In this work, a new multi-objective optimization algorithm called multi-objective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of moving graduated students from high school to college. The proposed technique produces a set of non-dominated solutions. To test the ability and efficacy of the proposed multi-objective algorithm, it is applied to a group of benchmarks and five real-world engineering optimization problems. Several widely used metrics are employed in the quantitative statistical comparisons. The proposed algorithm is compared with three multi-objective algorithms: Multi-Objective Water Cycle algorithm (MOWCA), Non-dominated Sorting Genetic algorithm (NSGA-II), and Multi-Objective Dragonfly algorithm (MODA). The produced results for the benchmarks and engineering problems show that in general the accuracy and diversity of the proposed algorithm are better compared to the MOWCA and MODA. However, the NSGA-II outperformed the proposed work in some of the cases and showed better accuracy and diversity. Nevertheless, in problems, such as coil compression spring design problem, the quality of solutions produced by the proposed algorithm outperformed all the participated algorithms. Moreover, in regard to the processing time, the proposed work provided better results compared with all the participated algorithms.

关键词： Multiobjective algorithms Multiobjective evolutionary algorithms MOLPB LPB Learner performance-based behavior algorithm optimization metaheuristic optimization algorithm

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Effect of fitness function on localization performance in range-free localization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第4期83卷 9761-9784页

作者： Han, Fengrong Abdelaziz, Izzeldin Ibrahim Mohamed Ghazali, Kamarul Hawari Zhao, Yue Baoji Univ Arts & Sci Sch Comp Baoji 721000 Peoples R China Univ Malaysia Pahang Coll Engn Kuantan 26300 Pahang Malaysia Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia Baoji Univ Arts & Sci Coll Geog & Environm Baoji 721016 Peoples R China

The problem of solving the nonlinear equations in the range-free localization algorithm has been transformed into an optimal solution problem. Meta-heuristic optimization method has been widely adopted to tackle above issues. How to choose the best localization fitness function for a specific target is a key factor in determining whether the localization algorithm is accurate or not. However, so far there is no literature to investigate the effect of fitness function on rang-free localization algorithm. Firstly, this study comprehensively reviews and classifies the frequently-used localization fitness function in range-free localization scheme. Next, multiple experiments are carried out for each typical localization fitness function. The experimental results are analyzed in terms of accuracy and stability. Besides, the advantage and disadvantage of each localization fitness function are also given. Finally, an advanced localization fitness function is proposed based on the above experimental results, which will provide a guide and reference for selection and improvement of the fitness function in range-free localization algorithm.

关键词： Wireless sensor network (WSNs) DV-Hop Localization algorithm metaheuristic optimization algorithm Fitness function Particle Swarm optimization (PSO)

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Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2024年第8期83卷 322-322页

作者： Wang, Yankun Sun, Xinshuang Wen, Tao Wang, Luqi Yangtze Univ Sch Geosci Wuhan 430100 Hubei Peoples R China Hubei Yangtze Univ Technol Dev Co Ltd Jiacha Cty Branch Shannan 856499 Xizang Peoples R China Minist Educ Engn Res Ctr Rock Soil Drilling & Excavat & Protec Wuhan 430074 Hubei Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China

This paper applies eight state-of-the-art metaheuristic optimization algorithms to optimize the kernel extreme learning machine (KELM) and compares the mean accuracy and stability of these algorithms in step-like displacement prediction of reservoir landslides. The Baishuihe landslide, Shuping landslide, and Baijiabao landslide were selected as study cases. A new index that can balance the goodness and error of predictions, the combined prediction precision criterion (CPC), was proposed for the evaluation of these algorithms. The results show that the mean prediction accuracy between different algorithms is small, up to only approximately 15%, but the difference between the prediction stabilities varies greatly, up to approximately 70%. All metaheuristic-optimized KELM models have high mean prediction accuracy, among which the horse herd optimization algorithm is better than the other algorithms in terms of both mean prediction accuracy and stability. Our results highlight the importance of ensemble predictions in attenuating the contingency of single metaheuristic-optimized machine learning (ML) models and improving their stability and demonstrate the impartiality of the CPC index in the comparative evaluation of models. The methodological framework of this article can provide a reference for future research on ML-based landslide displacement prediction.

关键词： metaheuristic optimization algorithm KELM Reservoir landslides Comparative study Displacement prediction

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Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony algorithm

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IEEE TRANSACTIONS ON POWER DELIVERY 2011年第4期26卷 2090-2101页

作者： Abu-Mouti, Fahad S. El-Hawary, M. E. Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3J 1Z1 Canada

Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application. This paper presents a new optimization approach that employs an artificial bee colony (ABC) algorithm to determine the optimal DG-unit's size, power factor, and location in order to minimize the total system real power loss. The ABC algorithm is a new metaheuristic, population-based optimization technique inspired by the intelligent foraging behavior of the honeybee swarm. To reveal the validity of the ABC algorithm, sample radial distribution feeder systems are examined with different test cases. Furthermore, the results obtained by the proposed ABC algorithm are compared with those attained via other methods. The outcomes verify that the ABC algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems. The ABC algorithm has only two parameters to be tuned. Therefore, the updating of the two parameters towards the most effective values has a higher likelihood of success than in other competing metaheuristic methods.

关键词： Artificial bee colony (ABC) distributed generation (DG) metaheuristic optimization algorithm power losses reduction

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Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network algorithm

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Computers, Materials & Continua 2023年第4期75卷 1073-1088页

作者： Musaed Alrashidi Department of Electrical Engineering College of EngineeringQassim UniversityBuraidahSaudi Arabia

Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging *** numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural Networkalgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation *** NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54.

关键词： Weibull probability density function wind energy numerical estimation method metaheuristic optimization algorithm neural network algorithm

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Frequency stabilization of a hybrid three-area power system equipped with energy storage units and renewable energy sources

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IET RENEWABLE POWER GENERATION 2022年第15期16卷 3267-3286页

作者： Elsaied, Mohamed Mostafa Abdel Hameed, Walid Helmy Hasanien, Hany M. Ain Shams Univ Elect Power & Machines Dept Cairo Egypt

This paper presents a three-area system tied together with tie lines. During the intervals of power mismatch, the system frequency deviates beyond the nominal value with an oscillatory response and the system may go to instability mode. The main purpose of the control strategy is optimizing the frequency fluctuations of the three areas and the deviations in the three tie line powers. This is achieved by three controllers as follows: the Tilt-Integral-Derivative (TID) controller, the Fractional Order Proportional-Integral-Derivative (FOPID), and the Proportional-Integral-Derivative (PID) controller. The three controllers are optimized by a new metaheuristic optimization algorithm based on the jellyfish behaviour in the ocean called the Jellyfish Search (JS) Optimizer. To prove the algorithm validity, it is compared with previous optimization techniques that have been applied to the study field such as Grey Wolf optimization (GWO) algorithm and Genetic algorithm (GA). Furthermore, renewable energy sources are implemented in the system such as wind energy and photovoltaic based on real data. Finally, energy storage devices (ESDs) like superconducting magnetic energy storage (SMES), capacitor energy storage (CES), and battery energy storage (BES) are implemented to improve the system behaviour due to the intermittent behaviour in the renewable sources.

关键词： three-term control power generation control renewable sources control strategy previous optimization techniques energy storage devices instability mode Proportional-Integral-Derivative controller system frequency deviates optimisation Fractional Order Proportional-Integral-Derivative oscillatory response energy storage frequency fluctuations control system synthesis tie line powers metaheuristic optimization algorithm power mismatch metaheuristics Jellyfish Search Optimizer capacitor energy storage tie lines algorithm validity frequency control Genetic algorithm system behaviour battery energy storage frequency stabilization superconducting magnet energy storage three-area system battery storage plants Tilt-Integral-Derivative controller renewable energy sources energy storage units three-area power system Grey Wolf optimization algorithm jellyfish behaviour genetic algorithms

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metaheuristic-based machine learning modeling of the compressive strength of concrete containing waste glass

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STRUCTURAL CONCRETE 2023年第4期24卷 5417-5440页

作者： Seghier, Mohamed El Amine Ben Golafshani, Emadaldin Mohammadi Jafari-Asl, Jafar Arashpour, Mehrdad Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Computat Mech Ho Chi Minh City Vietnam Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Monash Univ Dept Civil Engn Melbourne Vic Australia Univ Sistan & Baluchestan Fac Engn Dept Civil Engn Zahedan Iran

Waste glass (WG) can be used as fine aggregate and powder in concrete mixtures, preventing pollution induced by this non-biodegradable material. The properties of WG-included concrete should be examined before its practical use. Compressive strength (CS) is one of the most crucial characteristics of concrete, and the measurement of which needs time-consuming and expensive experiments. The use of machine learning (ML) methods for modeling the CS of concrete can help achieve more reliable and precise models. In this study, a comprehensive database of WG-included concrete was collected from the literature. Next, four ML methods, including support vector regression (SVR), least-square support vector regression (LSSVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron neural network (MLP) were served in the CS modeling. A recently proposed metaheuristic method, called marine predators algorithm (MPA), was proposed to optimize the control parameters of the ML models to guarantee generalized accuracy. Results indicate that the hybrid LSSVR-MPA model outperforms the other developed ML models comparing the error metrics with an RMSE = 2.447 MPa and R-2 = 0.983. The sensitivity analysis reveals that replacing the cement with WG powder decreases the CS, whereas serving the WG as the replacement for natural fine aggregate improves the CS.

关键词： compressive strength concrete machine learning metaheuristic optimization algorithm waste glass waste materials

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Wearable Sensor Based on Covalent Organic Framework Humidity Films for Long-Term Monitoring of Tomato Physiology Under Abiotic Stress

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ACS NANO 2024年第48期18卷 33105-33118页

作者： Huang, Liang He, Xinyang Hu, Jimin Qin, Caixun Huang, Chenxin Tang, Yu Zhong, Fenglin Kong, Xiangzeng Wei, Xuan Fujian Agr & Forestry Univ Coll Mech & Elect Engn Fujian Key Lab Agr Informat Sensoring Technol Fuzhou 350002 Fujian Peoples R China Natl Univ Singapore Dept Mat Sci & Engn Singapore 117576 Singapore Donghua Univ Coll Text Shanghai Frontier Sci Res Ctr Adv Text Shanghai 201620 Peoples R China Fujian Agr & Forestry Univ Coll Life Sci Fuzhou 350002 Peoples R China Fujian Agr & Forestry Univ Coll Hort Fuzhou 350002 Peoples R China Guangdong Polytech Normal Univ Acad Interdisciplinary Studies Guangzhou 510665 Peoples R China Fujian Agr & Forestry Univ State Key Lab Ecol Pest Control Fujian & Taiwan Cr Fuzhou 350002 Fujian Peoples R China

Global agricultural productivity is affected by plant stresses every year;as a consequence, monitoring and preventing plant stresses is a significant measure to protect the agro-ecological environment. Similar to the adoption of wearable devices to appraise human physiological information and disease diagnosis, however, in situ nondestructive monitoring of complex and weak physiological information in plants is an enormous challenge for the development of wearable sensors. Herein, to accurately analyze the changes of tomato internal information under multiple abiotic stresses in real-time, we introduce the covalent organic framework (COF) film synthesized by self-assembly layer by layer through the oil/water interface as a sensitive material to develop a multifilm-integrated wearable sensor capable of monitoring leaf surface humidity and leaf temperature. The flexible substrate can stretch with leaf growth to ensure the accuracy of long-term monitoring. Benefiting from the performance characteristics, such as ultrahigh sensitivity (S) of 0.8399 nA/%RH and an extremely low-resolution (Delta RH) value of 0.0564%, which could amplify the conducted signal, and the long-term stability of COFMOP-TAPB, the transpiration information on tomatoes under 10 abiotic stresses can be monitored continuously and with high precision over a long period by applying the COF-based sensor on the lower surface of the leaf at the upper end of the stem morphology. Finally, we employ metaheuristic optimization algorithms to predict the time series of the internal physiological change trend of tomatoes in the future so that farmers can take corresponding preventive measures in time to ensure the healthy growth of tomatoes.

关键词： plant wearable sensor plant physiological information covalent organic framework nonbiological stress metaheuristic optimization algorithm

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An Efficient Hybrid Scheduling Framework for Optimal Workload Execution in Federated Clouds to Maintain Performance SLAs

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JOURNAL OF GRID COMPUTING 2023年第3期21卷 1-22页

作者： Kshatriya, Divya Lepakshi, Vijayalakshmi A. REVA Univ Sch Comp Sci & Applicat Bengaluru 560064 Karnataka India

A Federated cloud is a composition of several clouds where a single federated cloud manager (FCM) is responsible for communication with the cloud service providers (CSPs) of associated clouds to accomplish the task of resource management. Finding optimal schedules for the incoming workloads from users is a highly complex task, as these workloads are expected to be completed under different deadlines. The existing frameworks for workload scheduling in federated cloud faced serious drawbacks such as inefficiency, unable to meet the deadlines assigned by users, etc. To overcome such drawbacks, this work introduces a new and effective strategy based on an optimization algorithm to achieve optimal scheduling of workloads. The proposed architecture involves a single FCM to identify the load in different clouds through communication with CSPs. A load level calculator (LLC) and a workload partitioning module (WPM) are maintained by the FCM to analyze the load in each cloud. Further, based on the load factor (LF) computed, the incoming workloads are partitioned into sub-queues of different sizes and are forwarded to the main clouds. The respective CSPs of the clouds maintain a cloud workload queue (CWQ) to locate the workloads and analyze the resource requirements. The CSP executes a scheduler based on hybrid flow-directed whale optimization (HFDWO) to find the optimal VMs in the data centre (DC) that can run the incoming workloads in the queue. The workloads are scheduled accordingly, and evaluations are conducted through simulations in the CloudSim tool. The performance of the approach is analyzed using the GWA T-12 Bitbrains dataset under different metrics. The overall improvement attained by the proposed approach compared to the existing frameworks is 25% in terms of SLA violation rate, 12% in terms of execution cost, 11% in terms of resource utilization, 33% in terms of makespan, 19% in terms of throughput and 28% in terms of response time.

关键词： Federated cloud Workloads Virtual machines Optimal workload scheduling metaheuristic optimization algorithm Load level calculation Workload partitioning

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