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Dynamic Service Placement in Edge Computing: A Comparative Evaluation of nature-inspired algorithms

IEEE ACCESS 2025年 13卷 2653-2670页

作者： Kazmi, Aqeel H. Staffolani, Alessandro Zhang, Tianhao Cabrera, Christian Clarke, Siobhan Trinity Coll Dublin Sch Comp Sci & Stat Dublin 2 Ireland Univ Bologna Dept Comp Sci & Engn I-40126 Bologna Italy Univ Cambridge Dept Comp Sci & Technol Cambridge CB2 1TN England

Edge computing has emerged as a promising solution for delivering services that demand low latency, high bandwidth, and stringent privacy requirements in numerous data- and compute-intensive applications, such as those in Smart Cities. Heterogeneity in edge computing resources and diverse application requirements demand adaptive optimization techniques, such as service placement, to conform to changing conditions. A service placement model must optimize the selection of edge nodes for deploying and executing services, thereby improving application QoS and maximizing resource utilization. Numerous optimization techniques for adaptive service placement problem have been proposed in the recent past. However, in most cases, the results have been evaluated in limited scenarios. This paper presents a comprehensive comparative study evaluating representative optimization algorithms applied to the problem of dynamic service placement across various application scenarios. The study covers nature-inspired approaches, including both meta-heuristics and reinforcement learning. Our experimental findings offer valuable insights into the strengths and weaknesses of the selected nature-inspired algorithms for service placement optimization, evaluated for applications with different QoS requirements. In our analysis, the Genetic Algorithm shows superior performance in achieving lower average distance and the average number of servers selected. Particle Swarm Optimization excels in minimizing average waiting time and placement decision time. The Artificial Bee Colony maintains low average latency, whereas the RL Proximal Policy Optimization demonstrates superior performance in terms of balancing the utilization of network resources.

关键词： Servers Cloud computing Edge computing Privacy Quality of service Microservice architectures Heuristic algorithms Metaheuristics Low latency communication Computational modeling dynamic service placement multi-objective optimization meta-heuristics nature-inspired algorithms service offloading computational offloading service scheduling

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An optimal design of droop controller of a De-loaded PV system using nature-inspired algorithms

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 4974-4996页

作者： Verma, Pankaj Katal, Nitish Dash, Sonali Pandit Deendayal Energy Univ Sch Energy Technol Elect Engn Gandhinagar 382007 Gujarat India Vellore Inst Technol Sch Elect Engn Chennai India Chandigarh Univ Dept Comp Sci Engn Mohali Punjab India

In photovoltaic (PV) penetrated systems, the active power support from PVs is expected during the load change situation to maintain the stability of the power system. The de-loading approach is common in PVs for generating an active power reserve, in this mode of operation (de-loading), the PVs are operated away from maximum power point resulting in an active power reserve. Most of the existing literature works have focused on developing an active power reserve in PVs but not on utilizing this reserve during a contingency condition. In this paper, a PV frequency regulation controller is developed which can optimally utilize the generated active power reserve for controlling the system frequency for load increase and decrease situations. In contrast to the other PV frequency regulation controllers, in this work, the gain value of the controller is not tuned using arbitrary or any other hit and trail methods, here, the value of the controller is tuned using the nature-inspired algorithms. Seven algorithms are used for obtaining the gain values;these values are tested on a single area and multi-area load frequency model in the SIMULINK software, the outputs obtained using these different values of gain are compared for different case scenarios. The obtained results indicate that for a sudden 15% load increase event in a single and multi-area models, a minimum frequency deviation of 0.3908 hz and 0.3076 hz is observed respectively when Firefly Algorithm is employed for gain optimization.

关键词： De-loading frequency regulation controller load frequency control maximum power point nature-inspired algorithms photovoltaic

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A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior

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COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-21页

作者： Robson, Adam Mistry, Kamlesh Woo, Wai-Lok Univ Sunderland Fac Business & Technol Sch Comp Sci & Engn Sunderland England Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne England

This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-inspired optimization algorithms and a mechanism that creates a continuing variance within population groupings, while attempting to maintain levels of computational simplicity that have helped nature-inspired optimization algorithms gain notoriety within the field of feature selection. Through this functionality, the proposed framework seeks to increase search diversity within the population swarm to address issues such as premature convergence, and oscillations within the swarm. The proposed frameworks have shown promising results when implemented into the Bat algorithm (BA), Firefly algorithm (FA), and Particle Swarm Optimization algorithm (PSO), all of which are popular when applied to the field of feature selection, and have been shown to perform well in a variety of domains, gaining notoriety due to their powerful search capabilities.

关键词： Classification Feature selection nature-inspired algorithms Optimization

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Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

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Frontiers of Structural and Civil Engineering 2021年第1期15卷 61-79页

作者： Ahmad SHARAFATI H.NADERPOUR Sinan Q.SALIH E.ONYARI Zaher Mundher YASEEN Department of Civil Engineering Science and Research BranchIslamic Azad UniversityTehranIran Faculty of Civil Engineering Semnan UniversitySemnan ***Iran Computer Science Department Dijlah University CollegeBaghdadIraq Department of Civil and Chemical Engineering College of ScienceEngineering and TechnologyUniversity of South AfricaJohannesburgSouth Africa Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam

Concrete compressive strength prediction is an essential process for material design and *** study investigates several novel hybrid adaptive neuro-fuzzy inference system(ANFIS)evolutionary models,i.e.,ANFIS-particle swarm optimization(PSO),ANFIS-ant colony,ANFIS-differential evolution(DE),and ANFIS-genetic algorithm to predict the foamed concrete compressive *** concrete properties,including cement content(C),oven dry density(O),water-to-binder ratio(W),and foamed volume(F)are used as input variables.A relevant data set is obtained from open-access published experimental investigations and used to build predictive *** performance of the proposed predictive models is evaluated based on the mean performance(MP),which is the mean value of several statistical error *** optimize each predictive model and its input variables,univariate(C,O,W,and F),bivariate(C-O,C-W,C-F,O-W,O-F,and W-F),trivariate(C-O-W,C-W-F,O-W-F),and four-variate(C-O-W-F)combinations of input variables are constructed for each *** results indicate that the best predictions obtained using the univariate,bivariate,trivariate,and four-variate models are ANFIS-DE-(O)(MP=0.96),ANFIS-PSO-(C-O)(MP=0.88),ANFIS-DE-(O-W-F)(MP=0.94),and ANFIS-PSO-(C-O-W-F)(MP=0.89),***-PSO-(C-O)yielded the best accurate prediction of compressive strength with an MP value of 0.96.

关键词： foamed concrete adaptive neuro fuzzy inference system nature-inspired algorithms prediction of compressive strength

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Utilization of nature-inspired algorithms for gas condensate reservoir optimization

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SOFT COMPUTING 2019年第14期23卷 5619-5631页

作者： Janiga, Damian Czarnota, Robert Stopa, Jerzy Wojnarowski, Pawel Kosowski, Piotr AGH Univ Sci & Technol Fac Drilling Oil & Gas PL-30059 Krakow Poland

Hydrocarbon production from a gas condensate reservoir has a major concern to the petroleum industry around the world. Due to pressure decline in a reservoir, liquid drops out of solution and cause a significant reduction of well productivity. As a result, huge amount of valuable oil stays underground. The proper production management for a gas condensate reservoir minimizes or eliminates the blocking problems. Optimization of condensate recovery can be achieved through appropriate selection of production and injection rates. However, determination of the optimal rates is a complex problem, involving many factors including geological uncertainty, rock petrophysical characteristics, fluid properties, economic costs, and technical ability. In this research, nature-inspired optimization algorithms are employed to evaluate the potential of production boost. Numerical testing and comparative study revealed that investigated natural-inspired algorithms outperform widely reported in the literature optimization methods and provide a higher quality solutions. The primary concern of this research is to develop an optimal gas condensate production strategy in terms of economic efficiency by utilization of nature-inspired optimization algorithm and providing their ability to generate better quality solution than engineering approach as well as gradient optimization.

关键词： nature-inspired algorithms Gas condensate reservoir Production optimization

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Hybridization between Neural Computing and nature-inspired algorithms for a Sentence Similarity Model Based on the Attention Mechanism

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2021年第1期20卷 1–21页

作者： Zhang, Peiying Huang, Xingzhe Li, Maozhen Xue, Yu China Univ Petr East China Coll Comp Sci & Technol 66 West Changjiang Rd Qingdao 266580 Peoples R China Brunel Univ Engn & Design London UB8 3PH England Nanjing Univ Informat Sci & Technol Sch Comp & Software 219 Ningliu Rd Nanjing 210044 Jiangsu Peoples R China

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.

关键词： Sentence similarity analysis nature-inspired algorithms attention mechanism convolutional neural network feature extraction

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A contemporary review of the applications of nature-inspired algorithms for optimal design of automatic generation control for multi-area power systems

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第2期51卷 187-218页

作者： Kalavani, Farshad Zamani-Gargari, Milad Mohammadi-Ivatloo, Behnam Rasouli, Mohammad Univ Tabriz Fac Elect & Comp Engn Room 10929 Bahman Blvd Tabriz Iran Penn State Behrend Erie PA 16563 USA

The modern electric grid is one the most complex man-made control systems. Proportional-integral-derivative (PID) controllers are widely used in a variety of applications including automatic generation control (AGC), automatic voltage regulators, power system stabilizers and flexible AC transmission system devices. Automatic generation control plays an important role in power system operation to maintain the frequency within an acceptable range and to properly respond to load changes under normal conditions. Using the PIDs, AGC keeps the balance between generation and load demand in order to minimize frequency deviations. Furthermore, the AGC regulates the tie-line power exchange and facilitates bilateral contracts spanning over several control areas, thus ensuring reliable operation of the interconnected transmission system. Since the power system load variations occur continually, generation control is set to automatic to restore the frequency after disturbances. The PID controllers have the advantage of simple structure, good stability, and high reliability. However, a robust and efficient tuning of PID parameters are still being investigated using different techniques. One of the recent areas of such studies is nature-inspired algorithms. The main objective of utilizing nature-inspired algorithms is to optimize parameters of several controllers simultaneously. This paper reviews the latest applications of various nature-inspired algorithms for optimal design of AGC control in power systems. Different algorithms, proposed in the recent literature, are classified based on the type of controller, objective function and test systems.

关键词： Automatic generation control (AGC) PID-controller nature-inspired algorithms Genetic algorithm Particle swarm optimization

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Hearing loss detection by discrete wavelet transform and multi-layer perceptron trained by nature-inspired algorithms

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第21-22期79卷 15717-15745页

作者： Yang, Jingyuan Govindaraj, Vishnu Varthanan Yang, Ming Wang, Shui-Hua Univ Leicester Dept Informat Leicester LE1 7RH Leics England Kalasalingam Acad Res & Educ Dept Biomed Engn Krishnankoil 626126 Tamil Nadu India Nanjing Med Univ Childrens Hosp Dept Radiol Nanjing 210008 Peoples R China Univ Leicester Dept Math Leicester LE1 7RH Leics England

(Aim) For detecting the hearing loss (HL) more accurately and efficiently, the new computer-aid diagnosis (CAD) based on a nature-inspired algorithm (NIAs) is proposed in this study. (Method) First, the discrete wavelet transform (DWT) is used for extracting texture features from the brain images, and then the principle component analysis (PCA) is employed to decrease the dimension of features. Second, the Multi-Layer Perceptron (MLP) is used as a classifier. Traditional gradient-based descent algorithms are vulnerable to get struck at local minima;thus, the NIAs are introduced. The differential evolution algorithm (DE), particle swarm optimization (PSO), artificial bee colony algorithm (ABC), and improved ABC (IABC) are employed to train MLP. Because the ordinary ABC is good at exploration but gives a poor performance at exploitation, therefore a new model of ABC, called IABC is proposed. The K-fold validation method is utilized to measure the performance of the CAD. (Result) To verify the performance of our method, The CAD based on IABC is compared with state-of-the-art-approaches. (Conclusion) The experiment results show that the overall accuracy of our method has the highest overall accuracy among five approaches. Therefore, the proposed CAD is effective method for detecting hearing loss.

关键词： Hearing loss Computer-aid diagnosis Discrete wavelet transform Multi-layer perceptron nature-inspired algorithms Improved artificial bee Colony algorithm K-fold cross validation

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Comparative Analysis of nature-inspired algorithms for Optimal Power Flow Problem: A Focus on Penalty-Vanishing Terms and Algorithm Performance

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IEEE ACCESS 2024年 12卷 29940-29958页

作者： Castanon, Gerardo Martinez-Herrera, Alberto F. Sarmiento, Ana Maria Aragon-Zavala, Alejandro Lezama, Fernando Tecnol Monterrey Sch Engn & Sci Monterrey 64849 Nuevo Leon Mexico Tecnol Monterrey Dept Elect & Mechatron Queretaro Campus Santiago De Queretaro 76130 Queretaro Mexico Polytechn Porto LASI Intelligent Syst Associate Lab GECAD Res Grp Intelligent Engn & Comp Adv Innovat P-4200072 Porto Portugal

This study presents a comparative analysis of multiple nature-inspired algorithms for solving the non-polynomial Optimal Power Flow (OPF) problem. Through numerical evaluations, we assess their performance across diverse objective functions, addressing complexities such as multi-fuel sources, valve point effects, and prohibited zones. The study involves the implementation of different nature-inspired heuristics and variants of the differential evolution algorithm to analyze their efficacy in solving the OPF problem within the context of large networks, specifically IEEE-30 and IEEE-57. The objectives of this research are threefold: (i) to determine the most effective nature-inspired algorithms for each case under consistent constraints, initial conditions, and using optimized parameters, (ii) to assess the success rate of penalty-vanishing terms concerning the penalized function versus the actual objective function, and (iii) to explore the impact of minor variations within a network on the behaviors, results, and profiles of penalty-vanishing terms. Utilizing a low-high sorting ranking method, considering mean, maximum, and minimum values for result computation and sorting, we identify the optimal algorithm among all those assessed for various objective functions, alongside assessing the success rate of penalty-vanishing terms. Our findings reveal that the differential evolution algorithm best version (DEAB) emerges as the most valuable solution.

关键词： Linear programming Optimization Costs Valves Testing Sorting Fuels Load flow analysis Performance evaluation Object recognition Heuristic algorithms Complexity theory Optimal control nature-inspired algorithms optimal power flow optimization penalty-vanishing terms success rate

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Comparing the performances of six nature-inspired algorithms on a real-world discrete optimization problem

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SOFT COMPUTING 2022年第21期26卷 11645-11667页

作者： Hakli, Huseyin Uguz, Harun Ortacay, Zeynep Necmettin Erbakan Univ Dept Comp Engn Konya Turkey Konya Tech Univ Dept Comp Engn Konya Turkey

Many new, nature-inspired optimization algorithms are proposed these days, and these algorithms are gaining popularity day by day. These algorithms are frequently preferred for these real-world problems as they need less information, are reliable and robust, and have a structure that can easily be applied to discrete problems. Too many algorithms result in difficulty choosing the correct technique for the problem, and selecting an unwise method affects the solution quality. In addition, some algorithms cannot be reliable for some specific real-world problems but very successful for others. In order to guide and give insight into the practitioners and researchers about this problem, studies involving the comparison and evaluation of the performance of algorithms are needed. In this study, the performances of six nature-inspired methods, which included five new implementations of differential evolutionary algorithms (DE), scatter search (SS), equilibrium optimizer (EO), marine predators algorithm (MPA), and honey badger algorithm (HBA) applied to land redistribution problem and genetic algorithms (GA), were compared. In order to compare the algorithms in detail, various performance indicators were used as problem based and algorithm based. Experimental results showed that DE and SS algorithms have a more successful performance than the other methods by solution quality, robustness, and many problem-based indicators.

关键词： Differential evolution algorithm Scatter search Discrete optimization Comparison nature-inspired algorithms

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