High power density and efficiency are key factors for automotive traction machines. One possible way to reach these goals is to increase the slot filling factor. As yet, most research has either been focused on optimi...
详细信息
ISBN:
(纸本)9781509029099
High power density and efficiency are key factors for automotive traction machines. One possible way to reach these goals is to increase the slot filling factor. As yet, most research has either been focused on optimizing the slot geometry with a given magnet wire diameter or on finding the optimal diameter for a given geometry. Oftentimes the submitted results lead either to a magnetically suboptimal stator geometry or the suggested winding pattern and geometries are not producible. The introduction of the needle winding technology, as an alternative to the insertion technology for the manufacturing of stators of automotive traction machines, enabled a defined wire placement in the slot. To use the full benefit of this advantage an optimal and producible winding layout is necessary. Therefore, in this article new optimization algorithms are proposed and compared to algorithms found in literature with regard to reachable slot filling factors and producibility. In a case study, the best performing algorithm was used to obtain an optimal combination of wire diameter and slot geometry to maximize the filling factor. With the proposed algorithm feasible winding patterns and slot geometries with an optimized filling factor can be obtained.
Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligen...
详细信息
Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in *** two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for *** proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including *** results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization *** results also prove the new algorithm's performance in solving real optimization problems in unknown search *** achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.
With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently usin...
详细信息
ISBN:
(纸本)9781665426329
With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently using the available spectrum resources in a wireless communication system. However, with the help of using various optimization algorithms, Cognitive Radio can manage and utilize the spectrum of resources more efficiently. This paper gives an overview of the state-of-art research that utilizes many optimization algorithms for different purposes such as sensing, allocating, sharing, and mobilizing the spectrum for better utilization and improving the throughput, convergence speed, delay, and minimization the interference. The main algorithms enclosed in this paper are Genetic algorithm, Particle Swarm optimization, Ant Colony optimization, and Artificial Bee Colony optimization algorithm.
Feature spaces optimization plays a very important role in object recognition and categorization. After analyzing of several fashionable local features at present, some optimization algorithms based on the information...
详细信息
ISBN:
(纸本)9783037851036
Feature spaces optimization plays a very important role in object recognition and categorization. After analyzing of several fashionable local features at present, some optimization algorithms based on the information theory are proposed. In this paper, we describe the approaches to recognize generic objects using these features which have been optimized. As baselines for comparison, we also implemented some additional recognition systems using other optimization algorithms. The performance analysis on the obtained experimental results demonstrates that the proposed optimization algorithms are effective and efficient.
To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design....
详细信息
To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 x D aerodynamic performance evaluations, where D denotes the number of design variables.
We propose the concept of thermal demultiplexer, which can split the heat flux in different frequency ranges intodifferent directions. We demonstrate this device concept in a honeycomb lattice with dangling atoms. Fro...
详细信息
We propose the concept of thermal demultiplexer, which can split the heat flux in different frequency ranges intodifferent directions. We demonstrate this device concept in a honeycomb lattice with dangling atoms. From the view ofeffective negative mass, we give a qualitative explanation of how the dangling atoms change the original transport *** first design a two-mass configuration thermal demultiplexer, and find that the heat flux can flow into different ports incorresponding frequency ranges roughly. Then, to improve the performance, we choose the suitable masses of danglingatoms and optimize the four-mass configuration with genetic algorithm. Finally, we give out the optimal configuration witha remarkable effect. Our study finds a way to selectively split spectrum-resolved heat to different ports as phonon splitter,which would provide a new means to manipulate phonons and heat, and to guide the design of phononic thermal devices inthe future.
In general, Metasurface Antennas (MSA) are designed to diminish the antenna shape by enhancing the operating band and directivity. As the efficiency decreases, the design complexity of MSA increases. In order to enhan...
详细信息
In general, Metasurface Antennas (MSA) are designed to diminish the antenna shape by enhancing the operating band and directivity. As the efficiency decreases, the design complexity of MSA increases. In order to enhance the antenna design, a high-gain MSA is designed using the hybrid African Vulture's optimization algorithm (AVOA), and the Capuchin Search algorithm (CapSA) is used for Radio Frequency (RF) energy harvesting. The dimensions of the designed antenna are \(1.66\lambda_optimization algorithm \times 1.25\lambda_optimization algorithm \times 0.02\lambda_optimization algorithm\) with a resonating frequency of 5 GHz. To design the high gain MSA, the proposed Hybrid African Vulture’s optimization and Capuchin Search algorithm (Hyb-AVOA-CapSA) is used to enhance the antenna parameters such as radiation efficiency, Bandwidth, gain, and return loss. Therefore, the proposed MSA design has achieved high efficiency and profit. Finally, the simulation has done on HFSS19 and ADS2020 version software; and evaluated using MATLAB. The proposed antenna gives a better efficiency of 70.12%, and resonate at 1.5 GHz of the axial ratio bandwidth at 5 GHz resonant frequency. The gain of the proposed antenna has increased from 6.86 to 7.6 dBi. While examining the comparative outcomes, the proposed approach has attained 22.4%, 23.7% high gain, and 18.85%, 12.6% lower return loss than the compared methods. Thus, the designed MSA is applied in RF energy harvesting applications because of its compact, low-profile, and simple structure. The Rectenna design uses a voltage doubler circuit at the receiver end and produces 5.55 V.
In order to obtain a Biogeography-Based optimization (BBO) algorithm with strong universal applicability, this paper presents a novel hybrid algorithm based on BBO and Grey Wolf Optimizer (GWO), named HBBOG. Firstly, ...
详细信息
In order to obtain a Biogeography-Based optimization (BBO) algorithm with strong universal applicability, this paper presents a novel hybrid algorithm based on BBO and Grey Wolf Optimizer (GWO), named HBBOG. Firstly, BBO and GWO are improved respectively. For BBO, the mutation operator is got rid of and a differential mutation operation is merged into the migration operator to enhance the global search ability. The original migration operation is replaced by a multi-migration operation to enhance the local search ability. For GWO, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima to some degree. Then, the improved BBO and the opposition learning based GWO are hybridized by a new strategy, named single-dimensional and all-dimensional alternating strategy, to formulate HBBOG. HBBOG can effectively maximize the two algorithms' advantages and overall balance exploration and exploitation, therefore, it can obtain strong universal applicability. We make a large number of experiments on a set of various kinds of benchmark functions and CEC2014 test set and apply HBBOG to clustering optimization. The experimental results show that HBBOG outperforms quite a few state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.
A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in ...
详细信息
A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at.
Lightweight research based on battery pack structural strength can improve the endurance and safety of electric vehicles. Based on the adaptive response surface and multi-objective particle swarm optimization algorith...
详细信息
Lightweight research based on battery pack structural strength can improve the endurance and safety of electric vehicles. Based on the adaptive response surface and multi-objective particle swarm optimization algorithm, this paper proposes an optimization design method for lightweight of battery pack shell. The thickness of the battery pack shell is the optimization parameter. The stress, strain, and frequency of the battery pack shell under typical working conditions are used as boundary conditions. The response surface model is established according to the criterion of cross terms in adaptive response surface method, and the multi-objective particle swarm optimization algorithm is used for iterative solution. The optimization results show that the maximum stress of the battery pack is reduced to the appropriate range, the first-order frequency is increased by 41% to reduce resonance, the maximum deformation is reduced from 2.7 to 1.12 mm, and the total mass is reduced by 26.8%. The battery pack optimization design method proposed in this paper can achieve lightweighting while meeting safety performance.
暂无评论