A new Multi Sine-Cosine algorithm for unconstrained optimization problems

Rehman, Muhammad Zubair; Khan, Abdullah; Ghazali, Rozaida; Aamir, Muhammad; Nawi, Nazri Mohd

doi:10.1371/journal.pone.0255269

Cited by 8 publications

(5 citation statements)

References 64 publications

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“…The results indicated the method’s efficacy in exploring and exploiting the search space, selecting the fewest feature subset possible, reducing datasets’ dimensions, and improving a high classification rate with low run-time on all datasets used. Moreover, Rehman et al [ 202 ] developed a novel Multi Sine Cosine algorithm (MSCA), which employed several swarm clusters to explore & exploit the search space to evade the local minima or maxima issues. The researchers evaluated the hybridization performance on different benchmark functions.…”

Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

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Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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“…The analysis of Table 4 shows that TN prediction activated carbon based on VELM inMAE = 5:04 mg/dm 3 and SCA inMAE = 8:17 mg/dm 3 , and fusion of VELM-SCA givesMAE = 4:23 mg/dm 3 , similar for MAPE and VELM inMAPE = 99:67 mg/dm 3 , and SCA inMAPE = 11:54 mg/d m 3 and hybrid of VELM-SCA givesMAPE = 8:45 mg/dm 3 . In addition, TP prediction activated carbon based on VELM inMAE = 1:31 mg/dm 3 and SCA inMAE = 2:61 mg/dm 3 , and hybrid of VELM-SCA givesMAE = 1:45 mg/dm 3 , similar for MAPE and VELM inMAPE = 12:77 mg/dm 3 and SCA inMAPE = 17:77 mg/dm 3 , and hybrid of VELM-SCA givesMAPE = 12:76 mg/dm 3 . Prediction of wastewater quality requires parametric indicators of TN, and TP shown that our proposed work VELM-SCA gives better performance.…”

Section: Root Mean Square Error (Rmse)mentioning

confidence: 93%

“…To improve the accuracy rate, this voting approach is used [2]. For improving the exploitation sine-cosine, the algorithm (SCA) allows to select the prediction of carbon by using search operations in a mathematical function of the sine and cosine [3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Alsolai

Asiri

Alabdan

et al. 2022

Adsorption Science & Technology

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Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97.

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“…Rehman et al [36] proposed a Multi Sine-Cosine algorithm in which multiple swarm clusters are utilized for diversification and intensification of the search space to avoid the local optima problem. This modified SCA was tested on benchmark numerical functions, and it performed better than basic SCA.…”

Section: Related Workmentioning

confidence: 99%

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

Bhattacharya

Nayak

et al. 2023

IEEE Access

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Meta-heuristics are commonly applied to solve various global optimization problems. In order to make the meta-heuristics performing a global search, balancing their exploration and exploration ability is still an open avenue. This manuscript proposes a novel Opposition-based learning scheme, called ''PCOBL'' (Partial Centroid Opposition-based Learning), inspired by the partial centroid. PCOBL aims to improve meta-heuristics performance through maintaining an effective balance between the exploration and exploitation. PCOBL was incorporated in three different meta-heuristics, and a comparative study was conducted on 28 CEC2013 benchmark problems with 30, 50, and 100 dimensions. In addition, we assessed the PCOBL in the IEEE CEC2011 real-world problems. The empirical results demonstrate that PCOBL balances the exploration and exploitation ability of the meta-heuristics, positively impacting their performance and making them outperform the state-of-the-art algorithms in terms of best-error runs and convergence in most of the optimization problems. Moreover, the computational cost analysis illustrated that the inclusion of PCOBL in the meta-heuristic algorithm has a low impact on its efficiency.

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A new Multi Sine-Cosine algorithm for unconstrained optimization problems

Cited by 8 publications

References 64 publications

Multiclass feature selection with metaheuristic optimization algorithms: a review

Multiclass feature selection with metaheuristic optimization algorithms: a review

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

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