scholarly journals Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bai Li
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Gold Price ◽  
Selection Strategy ◽  
Price Forecasting ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Previous Cycle ◽  
Subsequent Cycle

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

Hybrid Artificial Bee Colony Algorithm with Differential Evolution and Free Search for Numerical Function Optimization

2016 ◽  
Vol 25 (04) ◽  
pp. 1650020 ◽  
Author(s):  
Lian Lian ◽  
Fu Zaifeng ◽  
Yang Guangfei ◽  
Huang Yi
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Robustness Analysis ◽  
Selection Strategy ◽  
Local Optimum ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Free Search

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

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.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification

2017 ◽  
Vol 13 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Tutut Herawan ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Artificial Bee Colony ◽  
Classification Problem ◽  
Functional Link ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Learning Scheme ◽  
Feed Forward Network ◽  
Functional Link Neural Network

Functional Link Neural Network (FLNN) has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training. However, the standard Backpropagation (BP) learning uses for FLNN training prone to get trap in local minima which affect the FLNN classification performance. To recover the BP-learning drawback, this paper proposes an Artificial Bee Colony (ABC) optimization with modification on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN. This is motivated by good exploration and exploitation capabilities of searching optimal weight parameters exhibit by ABC algorithm. The result of the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared with the original FLNN architecture with standard Backpropagation (BP) (FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm provides better learning scheme for the FLNN network with average overall improvement of 4.29% as compared to FLNN-BP and FLNN-ABC.

scholarly journals An Artificial Bee Colony Algorithm with Random Location Updating

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lijun Sun ◽  
Tianfei Chen ◽  
Qiuwen Zhang
Keyword(s):  
Information Exchange ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Selection Strategy ◽  
Initial Population ◽  
Search Range ◽  
Bee Colony ◽  
Random Location ◽  
Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.

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