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Secure Computation Offloading Using enhanced genetic algorithm for Ocean IoT

JOURNAL OF GRID COMPUTING 2025年第1期23卷 1-19页

作者： Du, Ruizhong Zhao, Jianwei Gao, Yan Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Hebei Peoples R China Hebei Univ Hebei Prov Key Lab High Confidence Informat Syst Baoding 071000 Peoples R China Tianjin Univ Sch New Media & Commun Tianjin 300000 Peoples R China

The network nodes in the Ocean Internet of Things exhibit strong heterogeneous characteristics, which introduce complex and high-dimensional constraints for the optimization of task offloading in ocean mobile edge computing. To prevent data from being intercepted by malicious attackers during computation offloading and to protect client privacy, an offloading framework named the enhanced genetic algorithm for Secure Computation Offloading is proposed. The population initialization and crossover operators are optimized before the initialization phase to accelerate convergence and avoid the risk of excessive resource consumption due to cyclic scheduling. An elitism strategy is also introduced, along with dynamic mutation rate adjustment, to enhance the search efficiency and global optimization capability of the algorithm. The superiority of the proposed framework is validated through simulation experiments and a comprehensive comparison with other benchmark algorithms. Compared to the Simulated Annealing, Particle Swarm Optimization algorithms, the Min-Min algorithm, Basic genetic algorithm and Proximal Policy Optimization, the proposed scheduling algorithm demonstrates a significant enhancement in secure capacity during local computation, with improvements of 47.60%, 42.16%, 15%, 25% and 16.67%, respectively. Additionally, it achieves an increase in security during the computation offloading process by 16.54%, 16.22%, 14.29%, 12.56% and 10.02% respectively.

关键词： Ocean internet of things Mobile edge computing Secure computation offloading enhanced genetic algorithm Privacy protection

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Optimization design of concrete mix proportion based on support vector machine regression and enhanced genetic algorithm

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DISCOVER APPLIED SCIENCES 2025年第3期7卷 1-18页

作者： Zuo, Shuhong Liu, Benyi Heilongjiang Univ Sch Civil Engn Harbin 150080 Peoples R China Heilongjiang Prov Hydrol & Water Resources Ctr Harbin 150001 Peoples R China

Optimizing the selection of concrete proportion is the key to ensuring the strength, durability and cost-effectiveness of construction projects. However, the traditional mixed proportion method is costly, time-consuming and highly inconsistent. Therefore, this study aims to explore a new method to optimize the existing specific mixing ratio. In this study, support vector machine regression algorithm is used to establish a function that maps the existing composition data to the strength of concrete, and accurately predicts the performance of concrete. Then the predicted performance index is passed to the genetic algorithm. The experimental results show that the fitness value of HPO-SVM is 0.021, which is significantly lower than that of SSA-SVM and PSO-SVM, indicating the superiority of HPO in optimizing SVM. In addition, the average error value of HPO-SVM in specific proportion prediction is 1.226, which verifies the stability and accuracy of the prediction. The results show that the enhanced genetic algorithm has obvious advantages in improving convergence speed and solution quality. The concrete produced by the optimized mixture ratio has stronger compressive strength and bending strength. This study provides not only new methodological achievements, but also has important application value and popularization prospects in engineering practice and education, which is helpful to realize the goal of sustainable development of the construction industry and conforms to the development trend of green environmental protection.

关键词： Concrete proportion Proportional optimization method Support vector machine regression enhanced genetic algorithm

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Hybrid feature selection based on enhanced genetic algorithm for text categorization

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 49卷 31-47页

作者： Ghareb, Abdullah Saeed Abu Bakar, Azuraliza Hamdan, Abdul Razak Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Ukm Bangi 43600 Selangor Malaysia

This paper proposes hybrid feature selection approaches based on the genetic algorithm (GA). This approach uses a hybrid search technique that combines the advantages of filter feature selection methods with an enhanced GA (EGA) in a wrapper approach to handle the high dimensionality of the feature space and improve categorization performance simultaneously. First, we propose EGA by improving the crossover and mutation operators. The crossover operation is performed based on chromosome (feature subset) partitioning with term and document frequencies of chromosome entries (features), while the mutation is performed based on the classifier performance of the original parents and feature importance. Thus, the crossover and mutation operations are performed based on useful information instead of using probability and random selection. Second, we incorporate six well-known filter feature selection methods with the EGA to create hybrid feature selection approaches. In the hybrid approach, the EGA is applied to several feature subsets of different sizes, which are ranked in decreasing order based on their importance, and dimension reduction is carried out. The EGA operations are applied to the most important features that had the higher ranks. The effectiveness of the proposed approach is evaluated by using naive Bayes and associative classification on three different collections of Arabic text datasets. The experimental results show the superiority of EGA over GA, comparisons of GA with EGA showed that the latter achieved better results in terms of dimensionality reduction, time and categorization performance. Furthermore, six proposed hybrid FS approaches consisting of a filter method and the EGA are applied to various feature subsets. The results showed that these hybrid approaches are more effective than single filter methods for dimensionality reduction because they were able to produce a higher reduction rate without loss of categorization precision in most situation

关键词： Hybrid feature selection enhanced genetic algorithm Filter feature selection Text categorization

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Affine invariant matching of broken boundaries based on an enhanced genetic algorithm and distance transform

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IET COMPUTER VISION 2008年第3期2卷 142-149页

作者： Tsang, P. W. M. Yuen, T. Y. F. City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Past research work has shown that the process of shape matching can be rendered into an optimisation problem that determines, based on evolutionary algorithms, the best matching score between pairs of object boundaries. This important finding has enabled near planar objects to be identified efficiently when they are captured under different camera viewpoints. Among other evolutionary techniques, the genetic algorithm (GA) has demonstrated its feasibility in matching silhouette images of objects that are captured under a well-controlled environment. As the latter is not guaranteed in practice, the method has also been extended to match fragmented and incomplete contours. Despite the moderate success achieved, the overall performance is rather inconsistent and also varies significantly among different geometries. To overcome this problem, two variants of a novel approach based on the integration of a simple GA, the distance transform and the migrant principle are developed and presented. Experimental results reveal that the proposed methods are capable of matching incomplete and broken contours with a high success rate and exhibit good stability in performance.

关键词： object recognition evolutionary algorithms broken boundaries distance transform image matching affine invariant matching enhanced genetic algorithm genetic algorithms silhouette images matching

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Systematic design of multi-objective enhanced genetic algorithm optimized fractional order PID controller for sensorless brushless DC motor drive

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CIRCUIT WORLD 2022年第4期48卷 479-492页

作者： Kumarasamy, Vanchinathan Ramasamy, Valluvan KarumanchettyThottam Chinnaraj, Gnanavel Velalar Coll Engn & Technol Dept Elect & Elect Engn Erode India Velalar Coll Engn & Technol Dept Elect & Commun Engn Erode India AMET Deemed Be Univ Dept Elect & Elect Engn Kanathur India

Purpose The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control. Design/methodology/approach The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty. Findings This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort. Research limitations/implications The conventional real time issues such as nonlinearity characteristics, poor controllability and stability. Practical implications It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware expe

关键词： Circuit simulation Avionics Automatic positioning Brushless DC motor Fractional order PID controller BLDC motor drives enhanced genetic algorithm Sensorless speed control techniques

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Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization

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RENEWABLE ENERGY 2017年 107卷 508-521页

作者： Yin, Peng-Yeng Wu, Tsai-Hung Hsu, Ping-Yi Natl Chi Nan Univ Dept Informat Management Nantou 54561 Taiwan

Wind farm micro-siting is the decision problem for determining the optimal placement of wind turbines in consideration of the wake effect. Existing micro-siting models seek to minimize the cost of energy (COE). However, little literature addresses the production risk under wind uncertainty. To this end, we develop several versions of the simulation optimization based risk management (SORM) model which embeds the Monte Carlo simulation component for obtaining a large number of samples from the wind probability density function. Our SORM model is flexible and allowing the decision makers to conduct various forms of what-if analysis trading profit, cost and risk according to their business value. Then we propose an enhanced genetic algorithm (EGA) which is customized to the properties of wind farm dimensions. The experimental results show that the EGA can obtain the SORM decision both effectively and efficiently as compared to other metaheuristic approaches. We demonstrate how the risk under wind uncertainty can be effectively handled with our SORM models. The simulations with what-if analyses are conducted to disclose important characteristics of the risky micro-siting problem. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： enhanced genetic algorithm Micro-siting Risk management Simulation optimization What-if analysis Wind farm

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Optimum green plane masking for the contrast enhancement of retinal images using enhanced genetic algorithm

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OPTIK 2015年第18期126卷 1726-1730页

作者： Daniel, Ebenezer Anitha, J. Karunya Univ Dept Elect & Commun Engn Coimbatore 641114 Tamil Nadu India

Masking based techniques are well known and effective for contrast enhancement applications. The conventional unsharp masking in which fixed scale value is using irrespective of the types of test images. In this paper we propose an optimum green plane masking (OGPM) using enhanced genetic algorithm (EGA) for the contrast enhancement of retinal images. The green plane has more details of retinal images than the other two planes. The EGA can adaptively perform the selection, crossover and mutation of chromosomes. First, the proposed approach is evaluated using the standard test images and real time images for different contrast enhancement techniques and optimization techniques. Finally the proposed approach is used for the enhancement of retinal images. Results are analyzed using various performance measures and our OGPM shows better enhancement than other reported literature. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Contrast enhancement enhanced genetic algorithm Unsharp masking Retinal imaging

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Optimum Design of Simple Rotor System supported by Journal Bearing Using enhanced genetic algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2013年第9期14卷 1583-1589页

作者： Gu, Dong-Sik Kim, Young-Chan Lee, Jong-Myeong Choi, Byeong-Keun Changwon Moonsung Univ Div Shipbldg & Prod Chang Won 641771 Gyeongnam South Korea Doosan Heavy Ind Chang Won 642792 Gyeongnam South Korea Gyeongsang Natl Univ Grad Sch Engn Dept Energy & Mech Engn Tongyeong 6501660 Gyeongsnam South Korea Gyeongsang Natl Univ Inst Marine Ind Dept Energy & Mech Engn Tongyeong 6501660 Gyeongsnam South Korea

This paper presents a combined algorithm for optimum design of flexible rotor system supported by journal bearing. The proposed algorithm (enhanced genetic algorithm, EGA) is the synthesis of a modified genetic algorithm and simplex method A genetic algorithm (GA) is well known as a useful optimization technique for complex, nonlinear, and multi-optimization problems. The modified GA gives the candidate solutions in global search and then the solutions will be treated as initial values in the local search by the simplex method The EGA is not only faster than the standard genetic algorithm, but also provides a more accurate solution. In addition, this algorithm can find both the global and the local optimum solutions at the same time. Through two standard test functions, the advantages of the proposed hybrid algorithm has been confirmed Finally, to optimize a simple rotor system supported by journal bearing EGA is applied. The radial clearance, length to diameter ratio and average viscosity of the journal bearing are chosen as the design parameters. The objective function is the minimization of a maximum quality factor of a flexible rotor system in the operating speed range.

关键词： enhanced genetic algorithm Optimum design Flexible rotor system

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Using an enhanced genetic algorithm to solve the unit commitment problem

Using an enhanced genetic algorithm to solve the unit commit...

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IEEE International Conference on Intelligent Processing Systems

作者： Zhu, MY Cen, WH Wang, MY Zhang, PC Shanghai Jiao Tong Univ Dept Elect Power Engn Shanghai 200240 Peoples R China

ISBN: (纸本)0780342534

genetic algorithm (GA) is a general purpose optimization technique bused on mechanisms inspired from the natural genetics and natural selection. It is very suitable for solving nonlinear, multi-constraints, combinatorial optimization problems problems are tough for conventional methods. However, the simple genetic algorithm(SGA) may have a slow convergence or even cannot reach the global optimum. Therefore, an enhanced GA is proposed ill this paper to solve the unit commitment problem in power systems. The new features of the enhanced GA include chromosome mapping, problem specific operators and a local search technique. As expected, it has a significantly improved performance of finding the optimal solution to the unit commitment problem.

关键词： load distribution scheduling genetic algorithms power engineering computing problem solving convergence search problems enhanced genetic algorithm unit commitment general purpose optimization technique natural genetics natural selection nonlinear problem multiconstraint problem combinatorial optimization problems convergence global optimum power systems chromosome mapping problem specific operators local search technique performance

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Distribution System Reconfiguration Using Heuristic Method and enhanced genetic algorithm with Fuzzy Sets objectives 23

Distribution System Reconfiguration Using Heuristic Method a...

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23rd Iranian Conference on Electrical Engineering

作者： Kabirifar, Milad Shojaei, Azin Moallem, Mehdi Isfahan Univ Technol Esfahan Iran

ISBN: (纸本)9781479919710

Reconfiguration is an economic and appropriate method for minimizing power loss, improving power quality and increasing security and reliability of distribution system. By implementation of the smart infrastructure in distribution system, online reconfiguration is a suitable control tool in normal and abnormal conditions. This paper presents a heuristic method for real time multi objective reconfiguration based on fuzzy set objectives. In this method the number of switching operation is minimized and, hence, the search space and computational time are reduced. In another part of the work, enhanced genetic algorithm (EGA) is used to obtain more precise solution and then these two methods are compared.

关键词： distribution system reconfiguration heuristic method enhanced genetic algorithm fuzzy set theory

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