Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Yao, Liguo; Yang, Jun; Yuan, Panliang; Li, Guanghui; Lu, Yao; Zhang, Taihua

doi:10.3390/biomimetics8060492

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…Fisher score is a statistical measure used in feature selection to evaluate the discriminatory ability of features in distinguishing between different classes in a dataset [ 36 , 37 ]. The equation for Fisher score is given as follows:

where

is the variance among the means of different classes, while

is the average variance within each class.…”

Section: Methodsmentioning

confidence: 99%

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Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Yue,

Hu,

Zhu

2024

Biomimetics

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Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.

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where

is the variance among the means of different classes, while

is the average variance within each class.…”

Section: Methodsmentioning

confidence: 99%

“…Fisher score is a statistical measure used in feature selection to evaluate the discriminatory ability of features in distinguishing between different classes in a dataset [36,37]. The equation for Fisher score is given as follows:…”

Section: Feature Evaluationmentioning

confidence: 99%

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Yue,

Hu,

Zhu

2024

Biomimetics

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Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm

Chen,

2024

Can J Chem Eng

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The optimization of operating parameters for the simulated moving bed (SMB) is complex. A parameter optimization method for the SMB system was proposed based on the improved multi‐objective sand cat swarm optimization (IMOSCSO) algorithm. The multi‐objective sand cat swarm optimization (MOSCSO) algorithm integrated the update and selection mechanism of the repository in the multi‐objective algorithm. Three strategies were proposed to improve the traditional MOSCSO algorithm for increased population diversity, global search capability, and convergence speed. First, the cubic chaotic map was used to initialize the population, which improved the uniformity of the population distribution. Second, including a variable spiral search strategy in the prey search phase enabled the sand cat swarm to explore more search paths to adjust its position. Third, the convergence speed was enhanced by incorporating the alert mechanism of the sparrow search algorithm. The improved algorithm was tested with standard test functions. The IMOSCSO algorithm outperformed other algorithms in terms of convergence and accuracy. Finally, the IMOSCSO algorithm optimized the system parameters of the SMB, demonstrating its practical applications.

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Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization

Li,

Zhang,

Tsai

et al. 2024

Journal of Computational Design and Engineering

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Crayfish optimization algorithm (COA) is a novel, bionic, metaheuristic algorithm with high convergence speed and solution accuracy. However, in some complex optimization problems and real application scenarios, the performance of COA is not satisfactory. In order to overcome the challenges encountered by COA, such as being stuck in the local optimal and insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning (ASEG-OBL), competition-elimination, and chaos mutation. To evaluate the convergence accuracy, speed, and robustness of the modified crayfish optimization algorithm (MCOA), some simulation comparison experiments of 10 algorithms are conducted. Experimental results show that the MCOA achieved the minor Friedman test (FT) value in 23 test functions, CEC2014, and CEC2020, and achieved average superiority rates of 80.97%, 72.59%, and 71.11% in the Wilcoxon rank sum test (WT) respectively. In addition, MCOA shows high applicability and progressiveness in five engineering problems in actual industrial field. Moreover, MCOA achieved 80% and 100% superiority rate against COA on CEC2020 and the fixed-dimension function of 23 benchmark test functions. Finally, MCOA owns better convergence and population diversity.

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Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Cited by 3 publications

References 54 publications

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm

Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization

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