A novel search strategy based on gradient and distribution information for artificial bee colony algorithm

Qiu, Jianfeng; Xu, Mingjuan; Ming-hui, Liu; Xu, Wenhao; Wang, Jiwen; Su, Shoubao

doi:10.3233/jcm-170724

Cited by 3 publications

(4 citation statements)

References 25 publications

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“…In this research, since the gradient information not only provides a more accurate estimate of the global best solution for the GABC algorithm but also makes the sample screening more efficient, the exploitation is further enhanced. The findings of this paper can also apply to other GABC-based algorithms, such as SABC-GB [5], GABCS [22], MPGABC (a new GABC variant combing the novel search strategy with probability model) [26], WGABC (a linear weighted GABC algorithm) [27], and gdbABC (an improved GABC algorithm with gradient-based information) [31]. It is interesting that gdbABC controls the direction of search movement by a Newton-Raphson formula with the gradient information.…”

Section: Discussionmentioning

confidence: 87%

“…To avoid premature convergence, it uses a distribution-based strategy in case of several local suboptimal solutions. From Qiu's research [31], it can be found that gdbABC does not perform well for the optimizations of Rosenbrock and Griewank functions. Therefore, in order to enhance the applicability of the gradient-based algorithm, it is necessary to balance exploitation and exploration.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to gdbABC [31], ABC-SQP [15] and GdABC [16] also apply gradient information to Newton's method. As a result of searching along the gradient direction, they bring the results of new candidate solutions closer to the optimal value.…”

Section: Discussionmentioning

confidence: 99%

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A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

et al. 2020

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The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

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A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

et al. 2020

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“…However, its application in financial time series forecasts usually do not have good prediction owing to the randomness and intermittence in the data, the enhancement for forecasting accuracy and reliability of individual ELM still need further study. (3) The process of ABC optimization is easily fell into under-fitting solutions due to the uneven distribution of initial solutions in the data space [32]- [34], leading to the results of the algorithm lacked with stability and robustness. Besides, like other swarm intelligence algorithms, techniques to enhance the global search, as well as the local search ability of the whole algorithm, is subject to high demanding [28], [35], which aims to avoid phenomena such as precocious convergence, and trapping into local optima solution.…”

Section: Methodsmentioning

confidence: 99%

A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM

Ming

Ruan

et al. 2020

IEEE Access

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The practical significances and complexities of financial time series analysis induce highly demand more reliable hybrid model that denoised the data efficiently, handled with both linear and nonlinear patterns in the data, to achieve more accurate results. This paper suggests a new forecasting hybrid model for financial time series data combined Empirical Wavelet Transform (EWT) technique with improved Artificial Bee Colony (ABC) algorithm, Extreme Learning Machine (ELM) neural network, and Auto-Regressive Integrated Moving Average (ARIMA) linear analysis algorithm. The EWT is used to decompose and denoise the data to reconstruct the data more suitable for forecast. The improvement of the ABC algorithm is according to the Good Point Sets (GPS) theory and adaptive Elite-based Opposition (EO) strategy (GPS-EO-ABC) to overcome the drawbacks of the original algorithm and enhance the optimization performance. The optimized ELM with GPS-EO-ABC, as well as the ARIMA, are utilized independently to generated different forecasting results and combined by the weight-based procession. We testify the performance of the proposed improved ABC algorithm by ten benchmark functions, simulating the proposed forecasting models by three financial time-series datasets. The results indicate that: (1) The proposed algorithm shows outstanding capacities in parameter optimization. The optimized ELM generated more stable and precise results compared with original ELM, ABC-ELM, single LSTM, and ANN; (2) The proposed hybrid model has not only effectiveness but also efficiencies in denoising data, correcting outliers, coordinating both linear and nonlinear patterns, its performance in financial time series forecasting is more excellent than existing hybrid models.INDEX TERMS Financial time-series forecasting, EWT data prepossessing, ARIMA model, GPS-EO-ABC ELM model, hybrid model methodology.

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Single-station location algorithm based on secondary scatterer model

Zhao

Mao

2018

JCM

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A novel search strategy based on gradient and distribution information for artificial bee colony algorithm

Cited by 3 publications

References 25 publications

A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM

Single-station location algorithm based on secondary scatterer model

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