A Symmetric Sine Cosine Algorithm With Adaptive Probability Selection

Wang, Bin; Xiang, Tian; Li, Ning; He, Weiji; Li, Wei; Hei, Xinhong

doi:10.1109/access.2020.2970992

Cited by 12 publications

(13 citation statements)

References 33 publications

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“…Wang et al [34] have proposed an improved symmetric SCA with adaptive probability selection (SSCA-APS). APS was used to integrate original operators and the proposed operators.…”

Section: Literature Review a Related Workmentioning

confidence: 99%

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Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach

et al. 2020

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Dispatching rules are generally useful for scheduling jobs in flexible manufacturing systems (FMSs). However, the appropriateness of these rules relies heavily on the condition of the system; thus, there is no single rule that always outperforms others. In this state of affairs, diverse machine-learning technology offers an effective approach for dynamic scheduling, as it allows managers to identify the most suitable rule at each moment. Nonetheless, various machine-learning algorithms may provide diverse recommendations. The main objective of this study is to implement FMS scheduling using intelligent hybrid learning algorithms with metaheuristic improvements. The developed model involves three phases: feature extraction, optimal weighted feature extraction, and prediction. After the benchmark datasets for the FMS are gathered, feature extraction is performed using t-distributed stochastic neighbor embedding, linear discriminant analysis, linear square regression, and higher-order statistical features. Further, an optimal weighted feature extraction method is developed to select the optimal features with less correlation using the improved lion algorithm (LA), which is called the modified nomadic-based LA (MN-LA). Finally, the optimally selected weighted features are subjected to a hybrid learning algorithm with the integration of a fuzzy classifier and a deep belief network (DBN). For improving the prediction model, the membership function of the fuzzy classifier is optimized using the proposed MN-LA. Moreover, the activation function and the number of hidden neurons in the DBN are optimized using the MN-LA. The main objective of the optimized hybrid classifier is to enhance prediction accuracy. The experimental results indicate the effectiveness of the proposed heuristicbased scheduling method for FMSs.

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“…Wang et al [34] have proposed an improved symmetric SCA with adaptive probability selection (SSCA-APS). APS was used to integrate original operators and the proposed operators.…”

Section: Literature Review a Related Workmentioning

confidence: 99%

“…The limiting factor δ 1 is given by Eqs. (34) and (35), and MF LN indicates the number of linguistic variables.…”

Section: B Fuzzy Classifiermentioning

confidence: 99%

Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach

et al. 2020

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“…The periodic volatility of the trigonometric functions is used to construct an iterative equation that realizes the two thread functions of global search and local development. The succinct update iterative equation is used to impose disturbance and update the solution set [41]- [43].…”

Section: Introductionmentioning

confidence: 99%

“…The Improved SCA (ISCA) [52] was proposed, which changed the position searching equation by introducing an inertia weight, in order to avoid falling into local optima. Researchers proposed a novel improved symmetric SCA (SSCA-APS) to improve the algorithm performance [53]. In the proposed algorithm, adaptive probability selection was proposed.…”

Section: Introductionmentioning

confidence: 99%

A Modified Sine Cosine Algorithm for Solving Optimization Problems

Wang

2021

IEEE Access

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The sine cosine algorithm (SCA) is a newly emerging optimization algorithm. It is easy for sine cosine algorithm (SCA) to sink into premature of the algorithm and obtain a slower convergence rate when solving the complicated optimization problems, especially highly ill-posed problems. A novel modified sine cosine algorithm (MSCA) is put forward for solving the optimization problems. To our limited knowledge, the linear searching path and empirical parameter have not been applied to the related improved sine cosine algorithm. The proposed MSCA improves the search path of original SCA by introducing linear searching path and empirical parameter, effectively avoiding sinking into the local optimal. In addition, the proposed algorithm changes the definition of convergence factor. Two kinds of tests, including 23 benchmark functions test and actual engineering problem tests are adopted to prove the performance of the MSCA. In addition, the performance of the proposed MSCA is compared with SCA by using benchmark functions on different dimensional (D = 30, 50,100 and 500). As expected, the result of comparisons show that the proposed MSCA can better avoid the local optima than both the SCA and other population-based algorithms. And MSCA can obtain the faster convergence than SCA on different dimensions. INDEX TERMS Sine cosine algorithm, linear searching path, nature-inspired algorithm, optimization.

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“…In [68], Liu and Ma developed an improved GSA algorithm based on free search differential evolution, which enhanced the exploitation capability of the GSA algorithm. Wang et al [69], improved the exploitation capability of the sine cosine algorithm using an adaptive probability selection technique. In [70], an improved version of the TLBO algorithm has been proposed to enhance the searching ability and accuracy of the basic TLBO [37] algorithm.…”

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confidence: 99%

Border Collie Optimization

Dutta

Bhattacharyya

Dey³

et al. 2020

IEEE Access

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In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

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A Symmetric Sine Cosine Algorithm With Adaptive Probability Selection

Cited by 12 publications

References 33 publications

Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach

Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach

A Modified Sine Cosine Algorithm for Solving Optimization Problems

Border Collie Optimization

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