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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Abuthahir, S. Syed Peter, J. Selvin Paul Srm Institute of Science & Technology Department of Computing Technologies KTR Campus Chennai India

ISBN: (纸本)9798350370249

By utilizing the computational resources at the network edge, Vehicular Edge Computing (VEC) has become a viable paradigm to handle the computational demands of developing vehicular applications. A key component of VEC, task offloading seeks to improve vehicle services' Quality of Service (QoS) and maximize resource efficiency. This paper provides a thorough analysis of recent developments in VEC framework task offloading strategies, with an emphasis on the incorporation of meta heuristic algorithmic techniques. The review explains the opportunities and challenges in this field while delving into the basic ideas of task offloading and VEC. It looks at different meta heuristic algorithms such as Simulated Annealing (SA), Ant Colony optimization (ACO), Particle Swarm optimization (PSO), and Genetic algorithms (GA) and how to use them to optimize task offloading choices in VEC settings. This review also identifies emerging trends and challenges in the integration of meta heuristic algorithms for task offloading in VEC, as well as highlighting current research efforts in this area. It talks about important performance metrics like latency, energy efficiency, and reliability and assesses how well meta heuristic-based methods work to improve these metrics. In addition, it addresses open research questions and possible future paths towards the advancement of task offloading methods in VEC. Overall, this review offers practitioners and researchers useful insights into the role of meta heuristic algorithms in task offloading processes optimization in vehicular edge computing environments, guiding the development of dependable and effective vehicular edge computing systems. © 2024 IEEE.

关键词： Ant Colony optimization Edge Computing Genetic Algorithm meta heuristic algorithms Particle Swarm optimization simulated Annealing Task offloading

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A Survey of meta-heuristic Solution Methods for Mapping Problem in Network-on-Chips

A Survey of Meta-Heuristic Solution Methods for Mapping Prob...

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Janidarmian, Majid Fekr, Atena Roshan Islamic Azad Univ Sci & Res Branch CE Dept Tehran Iran

ISBN: (纸本)9783037853122

Network on Chip (NoC) has been proposed as a new paradigm for designing System on Chip which supports high degree of scalability and reusability. Mapping the IP cores onto a given platform is an important phase of NoC design which can greatly affect the performance and energy consumption of the chip. Mapping which is an instance of the constrained quadratic assignment problem (QAP) belongs to the class of NP-hard problems. Due to the complexity of many of these problems, particularly those of large sizes encountered in most practical settings, meta heuristic algorithms are conspicuously preferable. These algorithms help us achieve optimal or near optimal solutions in large size applications with reasonable time. In this paper eight types of Genetic algorithms (GA), Particle Swarm Optimization(PSO), Simulated Annealing(SA), Differential Evolution(DE) and Imperialist Competitive Algorithm (ICA) are applied in their basic frameworks for solving the mapping problem on two real core graphs Video Objective Plan Decoder and MPEG-4. The experimental results show the comparisons of these different meta heuristic algorithms with each other.

关键词： Network on Chip Mapping meta heuristic algorithms Genetic Algorithm Particle Swarm Optimization Differential Evolution Simulated Annealing Imperialist Competitive Algorithm

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Multi-Objective Differential Evolution for Design of Cascade Hydropower Reservoir Systems

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WATER RESOURCES MANAGEMENT 2018年第14期32卷 4779-4791页

作者： Yazdi, J. Moridi, A. Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

After Paris Agreement and obligation made by various countries to decrease greenhouse gases, generation of clean energy with low carbon was taken into consideration. Hydropower plant is considered as a clean, cheap and renewable energy source for generating electrical energy. Through the construction of the multipurpose dams and their optimal planning and management, we may decrease the potential losses sustained by aquatic ecosystem in addition to supplying the energy and fulfilling the industrial, agricultural and drinking water demands. In the present study, a multi-objective optimization model was proposed for determination of design parameters in cascade hydropower multi-purpose reservoir systems. Considering the significant number of constraints and decision variables and non-convex form of the objective functions and constraints, particularly in multi-reservoir systems, a multi-objective evolutionary algorithm (MOEA) known as non-dominated sorting differential evolution (NSDE) was developed to solve the problem and reduce the computational costs. Karkheh River basin was selected as a case study in order to make an assessment on the capabilities and strength of the model. This basin is capable of generating hydropower energy and agricultural development with high environmental considerations due to Hurolazim International Wetland. Based on the results, we may supply various demands such as environmental demands of the aquatic ecosystem with high reliability as well as generating firm hydropower energy through optimal design of cascade hydropower reservoirs.

关键词： Multi-purpose reservoir systems Genetic algorithm Firm energy meta heuristic algorithms Optimization Karkheh river basin

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CAMONET: Moth-Flame Optimization (MFO) Based Clustering Algorithm for VANETs

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IEEE ACCESS 2018年 6卷 48611-48624页

作者： Shah, Yasir Ali Habib, Hafiz Adnan Aadil, Farhan Khan, Muhammad Fahad Maqsood, Muazzam Nawaz, Tabassam COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Pakistan Univ Engn & Technol Taxila Dept Comp Engn Taxila 47080 Pakistan Univ Engn & Technol Taxila Software Engn Dept Taxila 47080 Pakistan

A network aggregated of wirelessly connected vehicles is recognized as vehicular ad hoc networks (VANETs). Clustering in vehicular network is a technique among many others, which targets to improve communication proficiency in VANETs. In each cluster, there is one cluster head (CH) used to manage the whole cluster. All the communications are accomplished by the CHs, i.e., inter-cluster and the intra-cluster communications. The efficiency of a network is measured by number of CHs, load on each CH and lifetime of clusters. In this paper, a novel Clustering Algorithm centered on Moth-Flame Optimization for VANETs (CAMONET) is anticipated. This is a nature-inspired algorithm. CAMONET generates optimized clusters for robust transmission. CAMONET is evaluated experimentally with renowned techniques, such as multiobjective particle swarm optimization, clustering algorithm based on ant colony optimization for VANETs, and comprehensive learning particle swarm optimization. To assess the comparative efficiency of these algorithms, numerous experiments are performed. The results are accomplished by modifying the values of grid size of the network, the number of nodes in the network, and the transmission range of nodes. The speed, direction, and transmission range of the nodes are the notable factors considered for optimized clustering. The results indicate that CAMONET delivers near optimal results that develops it into an efficient method to perform vehicular clustering in order to improve the overall performance of the network.

关键词： Intelligent transportation system (ITS) vehicular ad hoc networks (VANETs) ant colony optimization (ACO) moth-flame optimization (MFO) clustering meta heuristic algorithms population based algorithm

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Multi objective optimization of horizontal axis tidal current turbines, using meta heuristics algorithms

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ENERGY CONVERSION AND MANAGEMENT 2015年 103卷 487-498页

作者： Tahani, Mojtaba Babayan, Narek Astaraei, Fatemeh Razi Moghadam, Ali Univ Tehran Fac New Sci & Technol Tehran Iran Ruhr Univ Bochum Fac Civil & Environm Engn Sci Bochum Germany

The performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry. According to this fact, the optimum performance will be achieved by optimized geometry. In this research study, the multi objective optimization of the HATCT is carried out by using four different multi objective optimization algorithms and their performance is evaluated in combination with blade element momentum theory (BEM). The second version of non-dominated sorting genetic algorithm (NSGA-II), multi objective particle swarm optimization algorithm (MOPSO), multi objective cuckoo search algorithm (MOCS) and multi objective flower pollination algorithm (MOFPA) are the selected algorithms. The power coefficient and the produced torque on stationary blade are selected as objective functions and chord and twist distributions along the blade span are selected as decision variables. These algorithms are combined with the blade element momentum (BEM) theory for the purpose of achieving the best Pareto front. The obtained Pareto fronts are compared with each other. Different sets of experiments are carried out by considering different numbers of iterations, population size and tip speed ratios. The Pareto fronts which are achieved by MOFPA and NSGA-II have better quality in comparison to MOCS and MOPSO, but on the other hand a detail comparison between the first fronts of MOFPA and NSGA-II indicated that MOFPA algorithm can obtain the best Pareto front and can maximize the power coefficient up to 4.3% and the produced torque on stationary blade up to 57.9%. The geometries of the first and last members of the Pareto front of MOFPA are compared to each other. These members which produce the maximum power coefficient and the maximum produced torque on stationary blade have hyperbolic and constant chord distributions, respectively. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： HATCT Multi objective optimization meta heuristic algorithms BEM

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Optimal sizing of smart hybrid renewable energy system using different optimization algorithms

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ENERGY REPORTS 2022年 8卷 4935-4956页

作者： Mahmoud, Fayza S. Diab, Ahmed A. Zaki Ali, Ziad M. El-Sayed, Abou-Hashema M. Alquthami, Thamer Ahmed, Mahrous Ramadan, Husam A. Minia Univ Fac Engn Elect Engn Dept Al Minya 61111 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Wadi Addawaser 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt King Abdulaziz Univ Elect & Comp Engn Dept Jeddah 21589 Saudi Arabia Taif Univ Coll Engn Dept Elect Engn Taif 21944 Saudi Arabia

Hybrid renewable energy (HRE) sources have become a popular alternative in isolated and rural locations;because of the increased prices of fossil fuels and their CO2 emissions. This paper investigates the application of recent optimization methods to dedicate the optimal configuration of HRE sources. These HRE sources comprise photovoltaic (PV) panels, wind turbines (WT) with a battery storage system (BSS), and backup diesel generators (DGs). The cost function for the optimization problem has been chosen to be the cost of energy (COE) and the loss of power supply probability of the HRE system. The main function of the optimization algorithms is to minimize this cost function. However, the optimal configuration cannot be accomplished without fulfilling the system reliability and operational constraints. Salp swarm algorithms (SSA), the grey wolf optimizer (GWO), and the improved grey wolf optimizer (IGWO) were employed as optimization techniques. To assess the efficacy of these optimization techniques, they were applied using MATLAB simulation. A comparison of their outcomes was conducted in order to determine which technique was the most effective. Furthermore, a statistical study was carried out to determine the performance stability of each optimization strategy. The results revealed the optimal variables, including the number of PVs, WTs, battery banks (BBs) and the capacity of DGs. Over the course of a year, the optimized configuration was put to the test against a study of its capital and fuel expenses. IGWO showed the best performance among the three techniques, while the statistical results prove the robustness of the SSA algorithm. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Hybrid renewable energy systems PV WT meta heuristic algorithms GWO

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Cyclic storage approach for conjunctive operation of surface and groundwater systems (Case study: ZarrinehRoud catchment area)

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SOFT COMPUTING 2024年第3期28卷 1989-2014页

作者： Moeini, Ramtin Sarhadi, Kourosh Univ Isfahan Esfahan Iran

In this research, the problem of optimal conjunctive operation of surface and groundwater systems is investigated by proposing a cyclic storage approach. This problem is solved here using mathematical programming and some efficient meta heuristic algorithms. For this purpose, the mathematical model of this system is defined and first solved by the nonlinear programming (NLP) method. In addition, the performance of the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gravitational search algorithm (GSA) and, particle swarm optimization (PSO) algorithms are also studied to solve this problem. Here, two case studies, meaning a hypothetical benchmark system and conjunctive use of Buchan dam reservoir and Miandoab aquifer located in the catchment area of Urmia Lake (ZarrinehRoud catchment area) as a real problem, are considered to study the performance of proposed methods. In other words, for the hypothetical benchmark system, the results show that the optimal operating cost and related computational time are equal to 5.2428 Billion Rials and 5400 s, respectively, obtained by using the NLP method. In addition, in comparison with the result of the NLP method, the operation costs increased by 26.36%, 26.1%, 44.91% and, 21.28% using ABC, GA, GSA and, PSO algorithms, respectively. However, the computational time is extremely decreased in comparison with the related value of the NLP method using these algorithms for a particular case. In other words, for the real benchmark system, the results show that the optimal operating cost and related computational time are equal to 139.0145 Billion Rials and 259,200 s, respectively, obtained by using the NLP method. In addition, in comparison with the result of the NLP method, the operation costs increased by 43.74%, 32.32%, and 50.57% using ABC, GA and, PSO algorithms, respectively. However, by using this algorithm, the computational time is extremely decreased in comparison with the related value of the NLP method for a

关键词： Conjunctive operation Cyclic storage approach meta heuristic algorithms ZarinehRoud catchment area

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Revisiting a fuzzy rough economic order quantity model for deteriorating items considering quantity discount and prepayment

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MATHEMATICAL AND COMPUTER MODELLING 2013年第5-6期57卷 1466-1479页

作者： Taleizadeh, Ata Allah Wee, Hui-Ming Jolai, Fariborz Univ Tehran Coll Engn Dept Ind Engn Tehran Iran Chung Yuan Christian Univ Dept Ind & Syst Engn Chungli Taiwan

This study revisits a fuzzy rough economic order quantity (EOQ) model for deteriorating items considering quantity discount and prepayment. Since the proposed model is a mixed integer nonlinear programming type, it is solved using meta heuristic algorithms. Based on three different case examples, we have shown that our results using the Bees Colony Optimization (BCO) algorithm performs better than the other techniques which are used. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Inventory control EOQ Perishable Fuzzy rough meta heuristic algorithms

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Diagnosis and multiclass classification of diabetic retinopathy using enhanced multi thresholding optimization algorithms and improved Naive Bayes classifier

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第34期83卷 81325页

作者： Bhimavarapu, Usharani Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India

Early diagnosis is crucial to prevent a diabetic patient from being affected by blindness. Automatic and accurate detection of diabetic retinopathy is essential. A methodology for the detection and classification of diabetic retinopathy is presented in this paper. Data preprocessing methods are used to highlight subtle information to classify DR anomalies accurately. Image-enhancing techniques are used to boost image quality. Following the preprocessing stage, three main procedures are performed: segmentation, feature extraction, and classification. In contrast to brute force methods, metaheuristic algorithms can explore the solution space more quickly and provide precise, ideal solutions. Due to a lack of detailed image data, it is impossible to determine the precise limits based on image segmentation features. Threshold segmentation is the most effective choice for segmenting fundus images since it has benefits, including simple implementation, low computational complexity, and improved performance. A new variant of grasshopper optimization is proposed using the multi-thresholding version. The segmentation using the proposed model gives high accuracy, even for tiny lesions. A total of 41 features were extracted from the segmented fundus images. Finally, the improved Naive Bayes classifier classifies the various classes of diabetic retinopathy. The proposed methodology was trained and tested over the DIARETDB0, Messidor-2, Eye pacs-1, and APTOS datasets. The improved naive Bayes classifier enhanced the classification of diabetic retinopathy by an accuracy of 99.98% on the APTOS dataset, which was better than the previously existing techniques. The results proved that the improved naive Bayes classifier adequately diagnoses diabetic retinopathy from the retinal fundus images.

关键词： Diabetic retinopathy Fundus image Optimization meta heuristic algorithms

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Binary Artificial Electric Field Algorithm

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EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1155-1183页

作者： Chauhan, Dikshit Yadav, Anupam Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Math Jalandhar 144011 Punjab India

Artificial Electric Field Algorithm (AEFA) is one of the recent population-based optimization techniques, it is inspired by the electrostatic force theory. This article aims to design a novel binary version of the AEFA scheme to improve the performance of the original AEFA scheme in discrete space. The popular S-shaped and V-shaped functions are used to design the binary versions of the AEFA. The efficiency and the optimization ability of the proposed binary versions of the AEFA are studied theoretically as well as experimentally. An extensive experimental study is performed to understand the performance of the proposed schemes. A set of 24 benchmark problems are solved using binary versions of AEFA the experimental results are compared with nine state-of-the-art algorithms. The running time complexity and Wilcoxon's signed-rank statistical test are also conducted to judge the proposed algorithms. In addition to the experimental studies, theoretical analysis is also carried out which suggests the convergence scenario of the proposed schemes. These studies suggest that the designed binary versions of the AEFA are very efficient and competent in addressing difficult optimization problems.

关键词： meta heuristic algorithms Optimization Artificial Electric Field Algorithm Benchmark problems Binary algorithm S-shaped function V-shaped function

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