A new optimization algorithm based on chaotic maps and golden section search method

Koupaei, Javad Alikhani; Hosseini, Soodeh; Ghaini, F. M. Maalek

doi:10.1016/j.engappai.2016.01.034

Cited by 96 publications

(38 citation statements)

References 61 publications

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“…Experimental results show that the proposed golden section search method is valuable and gives a reasonable shape parameter beside satisfactory precision of the solution. Koupaei, et al (2016) proposed a practical version of golden section search algorithm to optimize objective functions. Consequently, their work presented an algorithm takes the benefits and capabilities for both of chaotic maps and the golden section search method in order to solve nonlinear optimization problems.…”

Section: Related Workmentioning

confidence: 99%

Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem

Barham¹,

Sharieh²,

Sleit³

2018

MAS

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Moth Flame Optimization (MFO) is one of the meta-heuristic algorithms that recently proposed. MFO is inspired from the method of moths' navigation in natural world which is called transverse orientation. This paper presents an improvement of MFO algorithm based on Golden Section Search method (GSS), namely GMFO. GSS is a search method aims at locating the best maximum or minimum point in the problem search space by narrowing the interval that containing this point iteratively until a particular accuracy is reached. In this paper, the GMFO algorithm is tested on fifteen benchmark functions. Then, GMFO is applied for link prediction problem on five datasets and compared with other well-regarded meta- heuristic algorithms. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Based on the experimental results, GMFO algorithm significantly improves the original MFO in solving most of benchmark functions and providing more accurate prediction results for link prediction problem for major datasets.

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Section: Related Workmentioning

confidence: 99%

Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem

Barham¹,

Sharieh²,

Sleit³

2018

MAS

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“…What is more, the forecasting process of this method (which is used to determine the smooth coefficient by the empirical estimation and trial-anderror methods) needs human intervention and thus has low automation and is an inefficient solving method. To overcome the drawback of the above two methods, the 0.618 method [28] can be used to search for the optimal smooth coefficient. However, the optimum result of the 0.618 method depends mainly on the objective function chosen.…”

Section: The Exponential Smoothing Methodmentioning

confidence: 99%

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Fan

Duan

et al. 2018

Mathematical Problems in Engineering

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In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

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“…However, weak local search ability of CSO limits its optimization performance; hence CSO can be improved by combining the strong search ability of other algorithms. Chaotic systems are famous for inherent ergodicity, irregularity, and pseudorandomness; these basic traits make it perform better than random operators in local searching [26]. Chaotic systems search extensively for solutions and can find a desirable solution within a practical time.…”

Section: Introductionmentioning

confidence: 99%

Improved Chicken Swarm Algorithms Based on Chaos Theory and Its Application in Wind Power Interval Prediction

Wang

Xianhui

et al. 2019

Mathematical Problems in Engineering

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Probabilistic interval prediction can be used to quantitatively analyse the uncertainty of wind energy. In this paper, a wind power interval prediction model based on chaotic chicken swarm optimization and extreme learning machine (CCSO-ELM) is proposed. Traditional optimization has limitations of low population diversity and a tendency to easily fall into local minima. To address these limitations, chaos theory is adopted in the chicken swarm optimization (CSO), which improves its performance and efficiency. In addition, the traditional cost function does not reflect the deviation degree of off-interval points; hence, an evaluation index considering the relative deviation of off-interval points is proposed in this paper. Finally, the new cost function is taken as the fitness function, the output layer weight of ELM is optimized using CCSO, and the lower upper bound estimation (LUBE) is adopted to output the prediction interval directly. The simulation result shows that the proposed method can effectively reduce the average bandwidth, improve the quality of interval prediction, and guarantee the interval coverage.

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A new optimization algorithm based on chaotic maps and golden section search method

Cited by 96 publications

References 61 publications

Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem

Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Improved Chicken Swarm Algorithms Based on Chaos Theory and Its Application in Wind Power Interval Prediction

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