RETRACTED ARTICLE: Sine–cosine algorithm for feature selection with elitism strategy and new updating mechanism

Gao

et al. 2020

J Image Video Proc.

In view of the problem that the conventional tracker does not adapt to abrupt motion, we propose a tracking algorithm based on the hybrid extended ant lion optimizer with sine cosine algorithm (EALO-SCA) in this paper. Firstly, the multiple elites is used to replace the single elite in the standard ant lion optimizer (ALO). The extended ALO (EALO) can enhance the global exploration ability, which can handle abrupt motion. Secondly, considering that sine cosine algorithm (SCA) has strong local exploitation operator, a hybrid EALO-SCA tracker is proposed using the advantages of both EALO and SCA. The proposed approach can improve tracking accuracy and efficiency. Finally, extensive experimental results in both quantitative and qualitative measures prove that the proposed algorithm is very competitive compared to 7 state-of-the-art trackers, especially for abrupt motion tracking.

Section: Introductionmentioning

confidence: 99%

Hybridizing extended ant lion optimizer with sine cosine algorithm approach for abrupt motion tracking

Gao

et al. 2020

J Image Video Proc.

“…On the other hand, a Sine Cosine Algorithm (SCA) [9] can also be considered as a potential tool for improving energy production of wind plant. This is because the SCA has been successfully solved various types of real world problems [10][11][12][13][14][15][16][17][18][19][20]. The essential features of the SCA algorithm is that the pseudo-code is slightly simple where the design variable is updated using only a random perturbation from the sine and cosine signals.…”

Section: Introductionmentioning

confidence: 99%

A modified sine cosine algorithm for improving wind plant energy production

Suid

Tumari

Ahmad

2019

IJEECS

This paper presents a Modified Sine Cosine Algorithm (M-SCA) to improve the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The two modifications employed to the original SCA are in terms of the updated step size gain and the updated design variable equation. Those modifications are expected to enhance the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-SCA is applied to maximize energy production of a row of ten turbines. The statistical performance analysis shows that the M-SCA provides the highest total energy production as compared to other existing methods.

Stat., optim. inf. comput.

“…Recently, the naturally inspired algorithms, such as genetic algorithm, particle swarm optimization algorithm, firefly algorithm, and crow search algorithm, have a great attraction and proved their efficiency as variable selection methods [12]. This is because that the main target in variable selection is to minimize the number of selected variables while maintaining the maximum accuracy of prediction, and, therefore, they can be considered as optimization problems [13].…”

Section: Introductionmentioning

confidence: 99%

Variable Selection in Count Data Regression Model based on Firefly Algorithm

Algamal

2019

Variable selection is a very helpful procedure for improving computational speed and prediction accuracy by identifying the most important variables that related to the response variable. Count data regression modeling has received much attention in several science fields in which the Poisson and negative binomial regression models are the most basic models. Firefly algorithm is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, firefly algorithm is proposed to perform variable selection for count data regression models. Extensive simulation studies and two real data applications are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. Further, its performance is compared with other methods. The results proved the efficiency of our proposed methods and it outperforms other popular methods.