In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication syste...
详细信息
In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication systems. Because a CR is capable of sensing the previous environmental communication information, CR decision engine plays the role of optimizing the individual transmission parameters. In order to obtain the allowable transmission power of multicarrier based CR system demands interference analysis a priori, for the sake of efficient optimization, a two-dimensional GA structure is proposed in this paper which enhances the computational complexity. Combined with the fitness objective evaluation standard, we focus on two multi-objectiveoptimization methods: The conventional GA applied with the multi-objective fitness approach and the non-dominated sorting GA with Pareto-optimal sorting fronts. After comparing the convergence performance of these algorithms, the transmission power of each subcarrier is proposed as non-interference emission with its optimal values in multicarrier based CR system.
Purpose - The purpose of this paper is to elaborate the algorithm and computer code for the structure optimization of the outer rotor permanent magnet brushless DC motor and to execute optimization of selected motor s...
详细信息
Purpose - The purpose of this paper is to elaborate the algorithm and computer code for the structure optimization of the outer rotor permanent magnet brushless DC motor and to execute optimization of selected motor structure using the non-deterministic procedure. Design/methodology/approach - The mathematical model of the device includes the electromagnetic field equations with the nonlinearity of the magnetic core taken into account. The numerical implementation is based on the finite element method and stepping procedure. The genetic algorithm has been applied for the optimization. The computer code has been elaborated. Findings - The elaborated computer software has been applied for the optimization and design of BLDC motors. The elaborated algorithm has been tested and a good convergence has been attained. Originality/value - The presented approach and computer software can be successfully applied to the design and optimization of different structure of BLDC motors.
This study presents a comprehensive framework for evaluating renewable and non-renewable power plants' performance using the Life Cycle Analysis (LCA) and emergy analysis. The emergy analysis is used to consider t...
详细信息
This study presents a comprehensive framework for evaluating renewable and non-renewable power plants' performance using the Life Cycle Analysis (LCA) and emergy analysis. The emergy analysis is used to consider the free ecosystem services in the sustainability of the systems as a supplement to the LCA. The results indicate that the wind and photovoltaic power plants have the best performance in terms of the LCA analysis, while the wind and combined cycle power plants have the highest emergy sustain ability index. The best scenario is chosen under a two-objectiveoptimizationproblem, including the single score and emergy sustainability as objective functions. Here, the wind power plant is the most interesting option while the combined cycle power plant with CO2 capture is the least interesting alternative. In this study, a novel framework is developed to assess the Cost of Avoided Carbon emissions (CACe) using the integrated LCA-emergy analysis for the combined cycle power plant in which its value is evaluated to be about $151.65/ton of CO2. Eventually, an uncertainty analysis is performed on the solar transformities using Monte Carlo simulation. The framework presented in this paper provides insights to increase power plants' sustainability for managers and authorities. (C) 2021 Elsevier Ltd. All rights reserved.
In this paper,we investigate the load balancing problem with the consideration of different qualityof-service(QoS) and channel state in 3GPP long term evolution(LTE) multi-cell *** imbalance among nearby cells often c...
详细信息
In this paper,we investigate the load balancing problem with the consideration of different qualityof-service(QoS) and channel state in 3GPP long term evolution(LTE) multi-cell *** imbalance among nearby cells often causes inefficient utilization of the system *** in overloaded cells,users cannot get satisfied services for short resources,while in idle cells,there is plenty of unoccupied ***,switching users only for load balancing with no consideration of channel state often results in aggressive handover of users,which may occupy too many resources in their target cells and deteriorate the network ***,how to efficiently perform load balancing with the consideration of channel state becomes an important ***,LTE network aims to serve users with different QoS requirements,which should be taken into account when performing load *** we propose a multi-objective optimization problem,whose objectives are load balancing index and network average load for users with QoS requirements and a unified utility function for users without QoS *** constraints are physical resource limits and users’ QoS *** we analyze its complexity and propose a practical algorithm,load balancing,with the consideration of different QoS requirements and channel state services(LBQC).Extensive simulations are *** results show that the proposed algorithm can approximate the optimal algorithm efficiently,and leads to significantly better performances than load balancing with no consideration of channel state,e.g.a lower new call blocking rate with a fewer system resources occupation for users with QoS requirements,a higher cell edge throughput and total throughput for users without QoS requirements.
One of the main concerns in rank aggregation tasks for metasearch service is how to retrieve and aggregate the large-scale candidate search results efficiently. Much work has been done to implement metasearch service ...
详细信息
One of the main concerns in rank aggregation tasks for metasearch service is how to retrieve and aggregate the large-scale candidate search results efficiently. Much work has been done to implement metasearch service engines with different rank aggregation algorithms. However, the performance of these metasearch engines can hardly be improved. In this paper, we transform the top-k ranking task into a multi-objective programming problem when user preferences are considered along with user queries. We build an improved discrete multi-objective programming model to make the aggregate rankings satisfy user queries and user preferences both, and then propose a user preferences-based rank aggregation algorithm accordingly. Based on discrete particle swarm optimization algorithm, we improve the encoding scheme, the initialization methods, the position and velocity definition, the integrating updating process, the turbulence operator, and the external archive updating and leader selection strategy to make sure the candidate results that fit the user's preferences can be located quickly and accurately in a large-scale discrete solution space. We have our proposed algorithm tested on three different benchmark datasets: a public dataset, the real-world datasets and the synthetic simulation datasets. The experimental results demonstrate the efficacy and convergence efficiency of the proposed algorithm over the baseline rank aggregation methods especially when dealing with large amount of candidate results. And when the set of candidate results is of normal size, the proposed algorithm is proved to perform not worse than the baseline methods.
A joint optimizationproblem of link-layer energy efficiency (EE) and effective capacity (EC) in a Nakagami-m fading channel under a delay-outage probability constraint and an average transmit power constraint is cons...
详细信息
A joint optimizationproblem of link-layer energy efficiency (EE) and effective capacity (EC) in a Nakagami-m fading channel under a delay-outage probability constraint and an average transmit power constraint is considered and investigated in this paper. First, a normalized multi-objective optimization problem (MOP) is formulated and transformed into a single-objectiveoptimizationproblem (SOP), by applying the weighted sum method. The formulated SOP is then proved to be continuously differentiable and strictly quasiconvex in the optimum average input power, which turns out to be a cup shape curve. Furthermore, the weighted quasiconvex tradeoff problem is solved by first using Charnes-Cooper transformation and then applying Karush-Kuhn-Tucker (KKT) conditions. The proposed optimal power allocation, which includes the optimal strategy for the link-layer EE-maximization problem and the EC-maximization problem as extreme cases, is proved to be sufficient for the Pareto optimal set of the original EE-EC MOP. Moreover, we prove that the optimum average power level monotonically decreases with the importance weight, but strictly increases with the normalization factor, the circuit power and the power amplifier efficiency. Simulation results confirm the analytical derivations and further show the effects of fading severeness and transmission power limit on the tradeoff performance.
Welding robot path planning gradually has increasingly widespread attention in automatic production on account of improving the production efficiency in the actual production process. It is a combinational optimizatio...
详细信息
Welding robot path planning gradually has increasingly widespread attention in automatic production on account of improving the production efficiency in the actual production process. It is a combinational optimizationproblem to find an optimal welding path for the robot manipulator by arranging the sequence and directions of welding seams. To solve the problem with two objectives, path length and energy consumption, this paper proposed an improved discrete MOEA/D based on a hybrid environment selection (DMOEA/D-HES) with a parallel scheme to search the optimal sequence and directions simultaneously for welding seams. The discretized reproduction and adaptive neighborhood provide a larger search range in solution space to overcome difficulties in duplication and uneven distribution of solutions. Adaptive decomposition method and improved hybrid environment selection promote solutions converge to the optimal direction and further balance convergence and diversity. Eight TSPLIB problems were tested with the proposed algorithm and the other four algorithms. Besides, the algorithm is compared with four multi-objective evolutionary algorithms (MOEAs) on the multi-objective welding robot path planning on the balance beam. The test results indicate DMOEA/D-HES outperforms other algorithms on convergence with a competitive diversity, which is effective to be applied in the actual welding process.
Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-g...
详细信息
Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi -objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentivebased DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33 -bus and 69 -bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.
Accurate discrete fracture network modeling is a significant requirement for fluid flow simulation in various applications such as managing groundwater resources, simulating oil and gas reservoirs, and modeling geothe...
详细信息
Accurate discrete fracture network modeling is a significant requirement for fluid flow simulation in various applications such as managing groundwater resources, simulating oil and gas reservoirs, and modeling geothermal energy resources. The existing fracture network modeling approaches are often unsuccessful in regenerating spatial variability and can only characterize the fracture geometries by statistical probability distributions. In addition, the alternative geostatistical methods to address these limitations suffer from a smoothing effect and reproducing fracture patterns due to the use of the two-point statistics technique. In this paper, a comparative study between the new object-based iterative fracture network modeling algorithm and the geostatistical direct sampling (DS) method is performed. The presented algorithm starts with an initial configuration to directly model the statistical geometry of the fracture network and uses particle swarm optimization algorithm to impose four different constraints and include its spatial variability. Each constraint is defined in the form of the difference between spatial properties of the reference configuration and of the generated model using L2-norm criterion characterized by common specific filtering functions in the image processing. Both employed methods are applied on a real 2-Dimentional fracture network image from an exposed wall and their performance is assessed by four different criteria including classification correctness rate (CCR), indicator variogram (gamma), degree of consistency (r), and average relative error (ARE). Results show the superiority of the presented algorithm over the DS method in regenerating the real fracture network configuration with CCR = 0.99, r = 0.994, ARE = 2.63, and the same indicator variogram function.
This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objectiveoptimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental...
详细信息
This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objectiveoptimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental and mating selections, named local coverage selection and local area selection, respectively, for multi-objective evolutionary algorithm. The local coverage selection strategy aims to preserve well-distributed individuals with good convergence. While, the local area selection strategy is devised to deliver a balanced evolutionary search. This is achieved by encouraging individuals for exploration or exploitation according to the I-epsilon+ indicator. In both strategies, a hierarchical clustering method is employed to discover the population structure. Based on the clustering results, in local coverage selection, the individuals of different clusters will be retained according to their coverage areas and crowding distances, such that distributing as evenly as possible in the Pareto front. In local area selection, the individual(s) with the best value of I-epsilon+ indicator in each cluster will be selected to perform mating, with the purpose of achieving a balanced exploration and exploitation. The proposed algorithm has been evaluated on 26 bench-mark problems and compared with related methods. The results clearly show the significance of the proposed method.
暂无评论