With the advent of smart vehicles, several new latency-critical and data-intensive applications are emerged in Vehicular Networks (VNs). Computation offloading has emerged as a viable option allowing ...to resort to the nearby edge servers for remote processing within a requested service latency requirement. Despite several advantages, computation offloading over resource-limited edge servers, together with vehicular mobility, is still a challenging problem to be solved. In particular, in order to avoid additional latency due to out-of-coverage operations, Vehicular Users (VUs) mobility introduces a bound on the amount of data to be offloaded towards nearby edge servers. Therefore, several approaches have been used for finding the correct amount of data to be offloaded. Among others, Federated Learning (FL) has been highlighted as one of the most promising solving techniques, given the data privacy concerns in VNs and limited communication resources. However, FL consumes resources during its operation and therefore incurs an additional burden on resource-constrained VUs. In this work, we aim to optimize the VN performance in terms of latency and energy consumption by considering both the FL and the computation offloading processes while selecting the proper number of FL iterations to be implemented. To this end, we first propose an FL-inspired distributed learning framework for computation offloading in VNs, and then develop a constrained optimization problem to jointly minimize the overall latency and the energy consumed. An evolutionary Genetic Algorithm is proposed for solving the problem in-hand and compared with some benchmarks. The simulation results show the effectiveness of the proposed approach in terms of latency and energy consumption.
Power converters systems aimed at renewable energy applications have become a common option for sustainable electricity and distributed generation, since their performance has improved, and prices ...have steadily been reduced in the last years. However, there are still several drawbacks that hinder their widespread installation, such as the simultaneous minimization of cost and volume, efficiency maximization, size reduction, etc. Quite often, accomplishing these goals requires dealing with complicated optimization problems, which are difficult to solve by classical methods. Metaheuristic techniques provide a viable alternative to solve complex intricate optimization problems, such as those encountered in the development of power electronics converters. This paper presents a comprehensive coverage of metaheuristic methodologies applied in the area of power converters. The review includes a classification of the methodologies and main objective functions in each paper surveyed. An aim for this paper is to highlight the importance of the optimization tools, and the many benefits they provide to tackle the challenges encountered in the design, operation, and control of power converters.
Optimizing K-coverage of mobile WSNs Elhoseny, Mohamed; Tharwat, Alaa; Yuan, Xiaohui ...
Expert systems with applications,
02/2018, Letnik:
92
Journal Article
Recenzirano
Recently, Wireless Sensor Networks (WSNs) are widely used for monitoring and tracking applications. Sensor mobility adds extra flexibility and greatly expands the application space. Due to the ...limited energy and battery lifetime for each sensor, it can remain active only for a limited amount of time. To avoid the drawbacks of the classical coverage model, especially if a sensor died, K-coverage model requires at least k sensor nodes monitor any target to consider it covered. This paper proposed a new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. Our model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that our proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted. The improvement that our proposed method achieved regarding the network lifetime was in a range of 26%-41.3% using stationary nodes while it was in a range of 29.3%-45.7% using mobile nodes. In addition, the network throughput is improved in a range of 13%-17.6%. Moreover, the running time to form the network structure and switch between nodes' modes is reduced by 12%.
Optimization of production medium is required to maximize the metabolite yield. This can be achieved by using a wide range of techniques from classical "one-factor-at-a-time" to modern statistical ...and mathematical techniques, viz. artificial neural network (ANN), genetic algorithm (GA) etc. Every technique comes with its own advantages and disadvantages, and despite drawbacks some techniques are applied to obtain best results. Use of various optimization techniques in combination also provides the desirable results. In this article an attempt has been made to review the currently used media optimization techniques applied during fermentation process of metabolite production. Comparative analysis of the merits and demerits of various conventional as well as modern optimization techniques have been done and logical selection basis for the designing of fermentation medium has been given in the present review. Overall, this review will provide the rationale for the selection of suitable optimization technique for media designing employed during the fermentation process of metabolite production.
The import and export of crude oil are vastly affected by the economy of a developing country. It can be useful for the production of petroleum products. Likewise, the developing country, India is ...completely relying on the import of crude oil from Gulf countries. Thus, there is a need for optimization of routes and modes of transportation. This article presents a two-stage cost and time minimizing fuzzy business restricted multi−objective multi−index transportation problem, in which supplies, demands, and requirements are triangular fuzzy numbers. A business restricted constraints using binary variables are added in the developed supply chain of crude oil in India. The proposed model helps the decision-maker to choose the source country for the import of crude oil. The model is solved by using our proposed fuzzy non−dominated sorting genetic algorithm (NSGA)−II. The performance of the proposed algorithm is analyzed by using a simulation technique for uncertainty level <inline-formula> <tex-math notation="LaTeX">\alpha \in {0, 1} </tex-math></inline-formula>. Also, the Pareto decision space for formulated business restricted transportation problem is discussed using <inline-formula> <tex-math notation="LaTeX">\alpha - </tex-math></inline-formula>cut technique. For the superiority of the proposed methodology, we have implemented this on real-world case study viz., Daya case study. Based on the results we claim that the methodology is superior.
This paper presents a boost-multilevel inverter design with integrated battery energy storage system for standalone application. The inverter consists of modular switched-battery cells and a ...full-bridge. It is multifunctional and has two modes of operation: 1) the charging mode, which charges the battery bank and 2) the inverter mode, which supplies ac power to the load. This inverter topology requires significantly less power switches compared to conventional topology such as cascaded H-bridge multilevel inverter, leading to reduced size/cost and improved reliability. To selectively eliminate low-order harmonics and control the desired fundamental component, nonlinear system equations are represented in fitness function through the manipulation of modulation index and the genetic algorithm (GA) is employed to find the optimum switching angles. A seven-level inverter prototype is implemented and experimental results are provided to verify the feasibility of the proposed inverter design.
We present a de novo discovery of an efficient catalyst of the Morita–Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower the estimated barrier of the rate‐determining ...step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We identify 435 candidates, virtually all of which contain an azetidine N as the catalytically active site, which is discovered by the GA. Two molecules are selected for further study based on their predicted synthetic accessibility and have predicted rate‐determining barriers that are lower than that of a known catalyst. Azetidines have not been used as catalysts for the MBH reaction. One suggested azetidine is successfully synthesized and showed an eightfold increase in activity over a commonly used catalyst. We believe this is the first experimentally verified de novo discovery of an efficient catalyst using a generative model.
An efficient catalyst of the Morita–Baylis–Hillman reaction was discovered using a graph‐based genetic algorithm. The catalytic activity was experimentally verified by a kinetic study and the newly discovered catalyst outcompetes a widely used catalyst for this reaction.
•An enhanced genetic algorithm (EGA) is proposed to reduce text dimensionality.•The proposed EGA outperformed the traditional genetic algorithm.•The EGA is incorporated with six filter feature ...selection methods to create hybrid feature selection approaches.•The proposed hybrid approaches outperformed the single filtering methods.
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 naïve 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 situations.
Feature selection methods are used to identify and remove irrelevant and redundant attributes from the original feature vector that do not have much contribution to enhance the performance of a ...predictive model. Meta-heuristic feature selection algorithms, used as a solution to this problem, need to have a good trade-off between exploitation and exploration of the search space. Genetic Algorithm (GA), a popular meta-heuristic algorithm, lacks exploitation capability, which in turn affects the local search ability of the algorithm. Basically, GA uses mutation operation to take care of exploitation which has certain limitations. As a result, GA gets stuck in local optima. To encounter this problem, in the present work, we have intelligently blended the Great Deluge Algorithm (GDA), a local search algorithm, with GA. Here GDA is used in place of mutation operation of the GA. Application of GDA yields a high degree of exploitation through the use of perturbation of candidate solutions. The proposed method is named as Deluge based Genetic Algorithm (DGA). We have applied the DGA on 15 publicly available standard datasets taken from the UCI dataset repository. To show the classifier independent nature of the proposed feature selection method, we have used 3 different classifiers namely K-Nearest Neighbour (KNN), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Comparison of DGA has been performed with other contemporary algorithms like the basic version of GA, Particle Swarm Optimisation (PSO), Simulated Annealing (SA) and Histogram based Multi-Objective GA (HMOGA). From the comparison results, it has been observed that DGA performs much better than others in most of the cases. Thus, our main contributions in this paper are introduction of a new variant of GA for FS which uses GDA to strengthen its exploitational ability and application of the proposed method on 15 well-known UCI datasets using KNN, MLP and SVM classifiers.
•A novel solar micro Combined Cooling, Heating, and Power cycle is proposed.•Parametric study is presented to investigate the effects of various parameters.•Thermodynamic and thermoeconomic ...optimizations of the desired system are conducted.•Multi-objective optimization technique is applied using Genetic Algorithm.
This paper proposes a novel micro solar Combined Cooling, Heating and Power (CCHP) cycle integrated with Organic Rankine Cycle (ORC) for summer and winter seasons. The thermal storage tank is installed to correct the mismatch between the supply of the solar energy and the demand of thermal source consumed by the CCHP subsystem, thus the desired system could continuously and stably operate. The cycle is analyzed and optimized from the viewpoint of thermodynamics and thermoeconomics. For summer mode, the thermal efficiency, exergy efficiency and product cost rate are found to be 23.66%, 9.51% and 5114.5$/year, while for winter mode, these values are 48.45%, 13.76% and 5688.1$/year, respectively. Five key parameters, namely turbine inlet temperature, turbine inlet pressure, turbine back pressure, evaporator temperature and heater outlet temperature are selected as the decision variables to examine the performance of the overall system. The thermal efficiency, exergy efficiency and total product cost rate are selected as three objective functions and Genetic Algorithm (GA) is employed to find the final solutions to both single and multi-objective optimizations of the system. The results indicate that in summer, thermal efficiency, exergy efficiency and total product cost rate in optimum case are improved to 28%, 27% and 17%, respectively, while in winter, these values are 4%, 13% and 4%.
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