A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem

Reddy, K.S.; Panwar, Lokesh Kumar; Panigrahi, B. K.; Kumar, Rajesh

doi:10.1007/s13369-017-2790-x

Cited by 68 publications

(26 citation statements)

References 27 publications

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“…A transformation function defines the likelihood of altering a position vector's elements from 0 to 1 and vice versa [48]. This section examines the functionality and performance of two different V-shaped and two different S-shaped transformation functions for developing different binary versions of MVO [49] [57].…”

Section: Binary Versions Of the Multi-verse Optimization Algorithmentioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.

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Section: Binary Versions Of the Multi-verse Optimization Algorithmentioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

IJIMAI

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“…Variables of this algorithm are bounded to 0 and 1, therefore each value of the solution is rounded to the nearest value to show the feature is selected or not. In the other work [39], a modified sigmoid function used as a mapped function to solve binary problems.…”

Section: Related Workmentioning

confidence: 99%

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

Taghian¹,

Nadimi-Shahraki²

2019

ACIJ

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A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.

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“…These two optimization algorithms had been applied in lots of fields successfully [38,39]. Subsequently, some improved methods were proposed [40][41][42][43]. Especially, Mirjalili presented that the SCA could be hybridized with other algorithms in the field of stochastic optimization to improve its performance in [37], and lots of hybrid methods had been applied [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Gao

Zhang

et al. 2020

J Image Video Proc.

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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.

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A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem

Cited by 68 publications

References 27 publications

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

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

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