scholarly journals The Simulation Design of Microwave Absorption Performance for the Multi-Layered Carbon-Based Nanocomposites Using Intelligent Optimization Algorithm

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1951
Author(s):  
Danfeng Zhang ◽  
Congai Han ◽  
Haiyan Zhang ◽  
Bi Zeng ◽  
Yun Zheng ◽  
...  
Keyword(s):  
Layer Structure ◽  
Optimal Solution ◽  
Single Layer ◽  
Simulation Design ◽  
Wide Bandwidth ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Absorbing Material ◽  
Important Means ◽  
Carbon Based

The optimal design objectives of the microwave absorbing (MA) materials are high absorption, wide bandwidth, light weight and thin thickness. However, it is difficult for single-layer MA materials to meet all of these requirements. Constructing multi-layer structure absorbing coating is an important means to improve performance of MA materials. The carbon-based nanocomposites are excellent MA materials. In this paper, genetic algorithm (GA) and artificial bee colony algorithm (ABC) are used to optimize the design of multi-layer materials. We selected ten kinds of materials to construct the multi-layer absorbing material and optimize the performance. Two algorithms were applied to optimize the two-layer MA material with a total thickness of 3 mm, and it was found that the optimal bandwidth was 8.12 GHz and reflectivity was −53.4 dB. When three layers of MA material with the same thickness are optimized, the ultra-wide bandwidth was 10.6 GHz and ultra-high reflectivity was −84.86 dB. The bandwidth and reflectivity of the optimized material are better than the single-layer material without optimization. Comparing the GA and the ABC algorithm, the ABC algorithm can obtain the optimal solution in the shortest time and highest efficiency. At present, no such results have been reported.

A Novel Global ABC Algorithm with Self-Perturbing

2017 ◽  
Vol 26 (4) ◽  
pp. 729-740 ◽  
Author(s):  
Shuliang Zhou ◽  
Dongqing Feng ◽  
Panpan Ding
Keyword(s):  
Optimal Solution ◽  
Population Based ◽  
Natural Phenomenon ◽  
Local Optimum ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Engineering Problems ◽  
Novel Method ◽  
Global Optimal

AbstractArtificial bee colony (ABC) is a kind of a metaheuristic population-based algorithms proposed in 2005. Due to its simple parameters and flexibility, the ABC algorithm is applied to engineering problems, algebra problems, and so on. However, its premature convergence and slow convergence speed are inherent shortcomings. Aiming at the shortcomings, a novel global ABC algorithm with self-perturbing (IGABC) is proposed in this paper. On the basis of the original search equation, IGABC adopts a novel self-adaptive search equation, introducing the guidance of the global optimal solution. The search method improves the convergence precision and the global search capacity. An excellent leader can lead the whole team to obtain more success. In order to obtain a better “leader,” IGABC proposes a novel method with global self-perturbing. To avoid falling into the local optimum, this paper designed a new mutation strategy that simulates the natural phenomenon of sick fish being eaten.

scholarly journals A Novel Music Emotion Recognition Model Using Neural Network Technology

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Yang
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Optimal Solution ◽  
Data Sets ◽  
Bp Network ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Local Optimal Solution

Music plays an extremely important role in people’s production and life. The amount of music is growing rapidly. At the same time, the demand for music organization, classification, and retrieval is also increasing. Paying more attention to the emotional expression of creators and the psychological characteristics of music are also indispensable personalized needs of users. The existing music emotion recognition (MER) methods have the following two challenges. First, the emotional color conveyed by the first music is constantly changing with the playback of the music, and it is difficult to accurately express the ups and downs of music emotion based on the analysis of the entire music. Second, it is difficult to analyze music emotions based on the pitch, length, and intensity of the notes, which can hardly reflect the soul and connotation of music. In this paper, an improved back propagation (BP) algorithm neural network is used to analyze music data. Because the traditional BP network tends to fall into local solutions, the selection of initial weights and thresholds directly affects the training effect. This paper introduces artificial bee colony (ABC) algorithm to improve the structure of BP neural network. The output value of the ABC algorithm is used as the weight and threshold of the BP neural network. The ABC algorithm is responsible for adjusting the weights and thresholds, and feeds back the optimal weights and thresholds to the BP neural network system. BP neural network with ABC algorithm can improve the global search ability of the BP network, while reducing the probability of the BP network falling into the local optimal solution, and the convergence speed is faster. Through experiments on public music data sets, the experimental results show that compared with other comparative models, the MER method used in this paper has better recognition effect and faster recognition speed.

An Optimization Model for Aircraft Maintenance Scheduling Based on ABC Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 147-151 ◽  
Author(s):  
Ling Ping Jiang
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Goal Function ◽  
Maintenance Scheduling ◽  
Aircraft Maintenance ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Scheduling Model ◽  
Airline Maintenance

The problem of airline maintenance scheduling is considered in this paper. A maintenance-scheduling model that can determine rational maintenance date is established, in this model, while aircraft materials as well as other factors are taken as constraints, and aircraft air-on-ground (AOG) loss is set as goal function. In order to solve the model, Artificial Bee Colony (ABC) algorithm is utilized by setting the appropriate number of the bees, which can find the optimal solution rapidly. Finally, airline’s practical data is applied to validate the feasibility and practicality of the model and ABC algorithm.

Effects of Varying Geometrical Parameters on Boiling From Microfabricated Enhanced Structures

2003 ◽  
Vol 125 (1) ◽  
pp. 103-109 ◽  
Author(s):  
C. Ramaswamy ◽  
Y. Joshi ◽  
W. Nakayama ◽  
W. B. Johnson
Keyword(s):  
Heat Dissipation ◽  
Layer Structure ◽  
Single Layer ◽  
Nucleate Boiling ◽  
Heat Fluxes ◽  
Working Fluid ◽  
Geometrical Parameters ◽  
Small Range ◽  
Two Phase ◽  
Wall Superheat

The current study involves two-phase cooling from enhanced structures whose dimensions have been changed systematically using microfabrication techniques. The aim is to optimize the dimensions to maximize the heat transfer. The enhanced structure used in this study consists of a stacked network of interconnecting channels making it highly porous. The effect of varying the pore size, pitch and height on the boiling performance was studied, with fluorocarbon FC-72 as the working fluid. While most of the previous studies on the mechanism of enhanced nucleate boiling have focused on a small range of wall superheats (0–4 K), the present study covers a wider range (as high as 30 K). A larger pore and smaller pitch resulted in higher heat dissipation at all heat fluxes. The effect of stacking multiple layers showed a proportional increase in heat dissipation (with additional layers) in a certain range of wall superheat values only. In the wall superheat range 8–13 K, no appreciable difference was observed between a single layer structure and a three layer structure. A fin effect combined with change in the boiling phenomenon within the sub-surface layers is proposed to explain this effect.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

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