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artificial bee colony-based combination approach to forecasting agricultural commodity prices

INTERNATIONAL JOURNAL OF FORECASTING 2022年第1期38卷 21-34页

作者： Wang, Jue Wang, Zhen Li, Xiang Zhou, Hao Chinese Acad Sci Acad Math & Syst Sci CFS MADIS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China

The fluctuation of agricultural commodity prices has attracted a considerable amount of attention. However, the complexity of the agricultural futures market and the variability of influencing factors makes the prediction of agricultural commodity futures prices difficult. We address the nonlinear characteristics of agricultural commodity futures price series by proposing a forecast combination approach based on a global optimization method, called the artificial bee colony algorithm (ABC), for forecasting soybean and corn futures prices. Firstly, we used three denoising techniques, namely singular spectral analysis (SSA), empirical mode decomposition (EMD), and variational mode decomposition (VMD), to filter the external noise in the original price series. Then, we generated diverse forecasting sub-models by combining denoising techniques and five popular forecasting models: autoregressive integrated moving average regression (ARIMA), support vector regression (SVR), recurrent neural network (RNN), gated recurrent neural network (GRU), and long-short term memory neural network (LSTM). Finally, we present an ABC approach for three forecast combinations: heterogeneous, semiheterogeneous, and homogeneous combination. Experimental results indicate that the semi-heterogeneous forecast combination is superior to other combination strategies. For corn and soybean prices, ABC-based semi-heterogeneous forecast combinations have error reductions of 53.3% and 50.0% of MAPE and improvements of 32.4% and 34.5% in Dstat compared to the best single models, respectively. (c) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

关键词： Agricultural commodity price Forecast combination Semi-heterogeneous combination artificial bee colony algorithm Denoising technique

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Global artificial bee colony algorithm for Boolean Function Classification

Global Artificial Bee Colony Algorithm for Boolean Function ...

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5th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Shah, Habib Ghazali, Rozaida Nawi, Nazri Mohd Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia

ISBN: (纸本)9783642365461

This paper proposed Global artificial bee colony algorithm for training Neural Network (NN), which is a globalised form of standard artificial bee colony algorithm. NN trained with the standard backpropagation (BP) algorithm normally utilizes computationally intensive training algorithms. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome, GABC algorithm used in this work to train MLP learning for classification problem, the performance of GABC is benchmarked against MLP training with the typical BP, ABC and Particle swarm optimization for boolean function classification. The experimental result shows that MLP-GABC performs better than that standard BP, ABC and PSO for the classification task.

关键词： artificial bee colony algorithm Back propagation Global artificial bee colony algorithm

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On the Performance of Internal Feedback artificial bee colony algorithm (IF-ABC) for Protein Secondary Structure Prediction

On the Performance of Internal Feedback Artificial Bee Colon...

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Li, Bai Gong, Ligang Yao, Yuan Zhejiang Univ Dept Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu Taiwan Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 30013 Taiwan

ISBN: (纸本)9781467363433

The prediction of protein secondary structures has been the focus of bioinformatics for decades. Since Christian Anfinsen's pioneering work, the prediction scheme has been transformed into a numerical optimization case, which purses for the lowest free-energy value. artificial bee colony algorithm (ABC) is a famous intelligence algorithm, and this work applies a novel ABC modified by internal feedback information (IF-ABC) for such optimization scheme. In IF-ABC, the convergence information is fully utilized as guidance for the subsequent convergence process. In addition, the original roulette selection strategy as in ABC is reasonably removed here. Comparable experiments are conducted on some sequences listed in the database of Protein Data Bank (PDB). Simulation results confirm, with statistical significance, that IF-ABC is more efficient and robust for such protein secondary structure prediction cases.

关键词： activity based costing Secondary Protein Structure internal feedback artificial bee colony algorithm PURSE

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The Combination of Bisection Method and artificial bee colony algorithm for Solving Hard Fix Point Problems

The Combination of Bisection Method and Artificial Bee Colon...

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7th Int Conf on Soft Comp Models in Industrial and Environm Applications

作者： Mansouri, P. Asady, B. Gupta, N. Islamic Azad Univ Dept Math Arak Branch Arak Iran Univ Delhi Dept Comp Sci Delhi India

ISBN: (纸本)9783642329210

In this work, with combination Bisection method and artificial bee colony algorithm together(BIS-ABC), We introduce the novel iteration method to solve the real-valued fix point problem f(x) = x, x is an element of [a, b] subset of R. Let f(a).f(b) < 0 and there exist alpha is an element of [a, b], f(alpha) = alpha. In this way, without computing derivative of function f, real-roots determined with this method that is faster than ABC algorithm. In numerical analysis, NewtonRaphson method is a method for finding successively better approximations to the simple real roots, if f'(x(i)) - 1 = 0,in i(th) iteration, then Newton's method will terminate and we need to change initial value of a root and do algorithm again to obtain better approximate of alpha. But in proposed method, we reach to solution with direct search in [a, b], that includes alpha(convergence speed maybe less than of Newton's method). We illustrate this method by offering some numerical examples and compare results with ABC algorithm.

关键词： Bisection method Root-finding method Fix point problems artificial bee colony algorithm

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An artificial bee colony algorithm for Fuzzy Portfolio Model with Concave Transaction Costs

An Artificial Bee Colony Algorithm for Fuzzy Portfolio Model...

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20th International Annual Conference on Management Science and Engineering

作者： Sun Meng-rong Chen Wei Capital Univ Econ & Business Sch Informat Beijing 100070 Peoples R China

ISBN: (纸本)9781479904716

In the financial market, the expected rates of security returns cannot be well reflected by historical data because of the high volatility of market environment. This paper deals with a fuzzy portfolio selection problem based on possibility theory. Due to ignoring transaction costs would result in inefficient portfolio, a possibilistic portfolio selection model with concave transaction cost is proposed. In addition, artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. To solve this nonlinear programming, the ABC algorithm is utilized. Finally, we illustrate the new model by a numerical example and compare results with Genetic algorithms (GA), which shows that the ABC algorithm is more effective and powerful than GA.

关键词： artificial bee colony algorithm concave transaction costs fuzzy portfolio possibility theory

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artificial bee colony Optimization Based Non-Intrusive Appliances Load Monitoring Technique in a Smart Home

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2021年第1期67卷 77-86页

作者： Ghosh, Soumyajit Chatterjee, Debashis Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Recent advances of energy management system in a smart home can lead to load monitoring of electrical appliances for energy saving and reduction of electricity bill. Thus, smart metering technology is getting widely implemented in several electricity distribution networks. Most of the existing research are concentrated towards individual power consumption of different household loads using some machine learning algorithm, which can increase computational burden of the processor. In this article, an improved method for estimation of the individual appliance current is carried out from a group of connected consumer electronics loads. The proposed method consists of two steps, first is to collect and store the current data of individual appliances with varying load for on line application. The second step is to estimate the individual load current using the stored data. In the proposed method, a search-based optimization, i.e., artificial bee colony (ABC) algorithm is used for the estimation of individual electrical load. Suitable simulations and experimental studies are carried out on a practical household system to demonstrate the suitability of the proposed methodology.

关键词： Home appliances Monitoring Optimization Switched mode power supplies Feature extraction Smart meters Estimation Non-intrusive load monitoring smart metering home energy management artificial bee colony algorithm load identification

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Joint Angle-frequency Estimation Based on WSF using artificial bee colony algorithm

Joint Angle-frequency Estimation Based on WSF using Artifici...

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International Conference on Information Science and Technology (ICIST)

作者： Zhang, Zhicheng Lin, Jun Shi, Yaowu Jilin Univ Coll Commun Engn Changchun 130023 Peoples R China Jilin Univ Coll Instrumentat & Elect Engn Changchun 130023 Peoples R China

ISBN: (纸本)9781467351379

In this paper, the Weighted Subspace Fitting (WSF) method using artificial bee colony (ABC) algorithm is applied to the problem of estimating the direction-of-arrival (DOA) and the Doppler frequency of multiple signal sources impinging on an antenna array simultaneously. The extended observability matrix containing the angle and frequency parameters is established using state-space model. The WSF method is used to estimate the parameters accurately and convert the problem from parameter estimation to a nonlinear multidimensional function optimization. Then artificial bee colony algorithm is applied to optimize the function and estimate the DOA and Doppler frequency. The proposed method reserves the asymptotic unbiased estimation of the WSF method and reduces the computational burden of calculating the solution. Besides, the estimated parameters can be paired automatically.

关键词： Doppler frequency artificial bee colony algorithm United States Department of Agriculture Parameter estimation Function multiple signal Frequency estimation

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Automatic Method for Stock Trading Combining Technical Analysis and the artificial bee colony algorithm

Automatic Method for Stock Trading Combining Technical Analy...

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IEEE Congress on Evolutionary Computation

作者： Brasileiro, Rodrigo C. Souza, Victor L. F. Femandes, Bruno J. T. Oliveira, Adriano L. I. Univ Fed Pernambuco Ctr Informat Recife PE Brazil Univ Pernambuco Poltechn Sch Recife PE Brazil

ISBN: (纸本)9781479904549;9781479904532

There are many researches on forecasting time series for building trading systems for financial markets. Some of these studies have shown that it is possible to obtain satisfactory results, thereby contradicting the theory of Efficient Markets Hypothesis (EMH) that suggests that prices are randomly generated over time. This paper proposes an intelligent system based on historical closing prices that uses technical analysis, the artificial bee colony algorithm (ABC), a selection of past values (lags), nearest neighbor classification (k-NN) and its variation, the Adaptative Classification and Nearest Neighbor (A-k-NN). A very important step for time series prediction is the correct selection of the past observations (lags). Our method uses this strategy since it uses the k-NN and A-k-NN to decide on the buy and sell points, combined with the ABC algorithm which is used to search for the best parameter settings of system and a good set of lags. This paper compares the results obtained by the proposed method with the buy and hold strategy and with other work that performed similar experiments with the same trading model and the same stocks. The key measure for performance comparison is the profitability in the analyzed period. The proposed method generates much larger profits compared to the other method and to the buy and hold strategy. Our method outperforms the other methods in thirteen out of the fifteen stocks tested, minimizing the risk of market exposure.

关键词： stock trends predicting financial forecasting machine learning artificial bee colony algorithm selection of Tags stock market

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Parameter Tuning of artificial bee colony algorithm for Gaussian Noise Elimination on Digital Images

Parameter Tuning of Artificial Bee Colony Algorithm for Gaus...

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IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)

作者： Kockanat, Serdar Karaboga, Nurhan Cumhuriyet Univ Sivas Vocat Sch Sivas Turkey Erciyes Univ Fac Engn Dept Elect & Elect Engn Kayseri Turkey

ISBN: (纸本)9781479906611;9781479906598

In this paper, the control parameters of the artificial bee colony algorithm were examined to determine for the best performance of the noise elimination problem on gray level digital images. In order to eliminate a noise, a two dimensional finite impulse response digital filter was designed and the artificial bee colony algorithm was used to adjust its coefficient matrix. For the best selected control parameters, the designed two dimensional finite impulse response digital filter was used to eliminate the Gaussian noise on the gray level digital images at different noise densities and the performance of the designed filter was compared in terms of the noise tolerance.

关键词： artificial bee colony algorithm gaussian noise noise elimination two dimensional digital filter

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Week Ahead Electricity Price Forecasting Using artificial bee colony Optimized Extreme Learning Machine with Wavelet Decomposition

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第2期28卷 556-567页

作者： Udaiyakumar, S. Victoire, Aruldoss Albert T. Sri Ramakrishna Inst Technol Coimbatore 641010 Tamil Nadu India Anna Univ Reg Campus Coimbatore Maruthamalai Main Rd Coimbatore 641046 Tamil Nadu India

Electricity price forecasting is one of the more complex processes, due to its non-linearity and highly varying nature. However, in today's deregulated market and smart grid environment, the forecasted price is one of the important data sources used by producers in the bidding process. It also helps the consumer know the hourly price in order to manage the monthly electricity price. In this paper, a novel electricity price forecasting method is presented, based on the artificial bee colony optimized Extreme Learning Machine (ABC-ELM) with wavelet decomposition technique. This has been attempted with two different input data formats. Each data format is decomposed using wavelet decomposition, Daubechies Db4 at level 6;all the decomposed data are forecasted using the proposed method and aggregate is formed for the final prediction. This prediction has been attempted in three different electricity markets, in Finland, Switzerland and India. The forecasted values of the three different countries, using the proposed method are compared with various other methods, using graph plots and error metrics and the proposed method is found to provide better accuracy.

关键词： artificial bee colony algorithm Electricity Price Forecasting Extreme Learning Machine Neural Network Single Hidden Layer Neural Network

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