Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions

Sharma, Pankaj; Raju, Saravanakumar

doi:10.1007/s00500-023-09276-5

Cited by 17 publications

(4 citation statements)

References 232 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess the effectiveness and innovation of the ISSA, simulation experiments were conducted using four benchmark test functions listed in Table 1 [33]. Specifically, F1 and F2 represent unimodal functions, while F3 and F4 represent complex multimodal functions.…”

Section: Performance Test Of Issamentioning

confidence: 99%

Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM

Wang,

Wang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study aimed to develop and validate an improved sparrow search algorithm (ISSA)-optimized Least Squares Support Vector Machine (LSSVM) model for accurately predicting the tooth profile deviation of rigid gears produced by wire electrical discharge machining (WEDM). The ISSA was obtained by optimizing the sparrow search algorithm (SSA) using Tent chaotic mapping, adaptive adjustment strategy, dynamic inertia weights, and grey wolf hierarchy strategy. The effectiveness of the ISSA was verified using four different classes of benchmark test functions. Four main process parameters (peak current, pulse width, pulse interval, and tracking) were taken as inputs and the tooth profile deviations of rigid gears were considered as outputs to develop an ISSA-LSSVM-based profile deviation prediction model. The prediction performance of the ISSA-LSSVM model was evaluated by comparing it with the LSSVM model optimized by three standard algorithms. The prediction results of the ISSA-LSSVM model were R2 = 0.9828, RMSE = 0.0029, and MAPE = 0.0156. The results showed that the established model exhibits high prediction accuracy and can provide reliable theoretical guidance for predicting the tooth profile deviation of rigid gears.

show abstract

Section: Performance Test Of Issamentioning

confidence: 99%

Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM

Wang,

Wang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The results are presented in Figure 8. To compare the quality of the proposed models, residuals, i.e., the differences between the experimental data and model-predicted values of data, were calculated based on Equation (5). The obtained J values (errors) are presented in Table 7.…”

Section: Verification Of the Obtained Mathematical Model Of E Coli Bl...mentioning

confidence: 99%

“…Metaheuristic algorithms have shown promising performance on such complicated tasks [3]. Both single and population-based metaheuristics stand out as effective alternatives to traditional optimization methods [4,5]. They tackle various optimization problems Classical CSA using the VSA evolution mechanism to revise and exploit the solution space [37], 2021 2 0.1, 0.5…”

Section: Introductionmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

View full text Add to dashboard Cite

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

show abstract

“…Metaheuristic algorithms have been successfully used to solve complex engineering and science computation problems, such as function optimization [ 4 , 5 , 6 ], engineering optimization [ 7 , 8 , 9 ], and feature selection problems [ 10 ]. Researchers have proposed binary metaheuristic algorithms or improved versions for feature selection, such as binary swarm optimization (BPSO) [ 11 ], the binary artificial bee colony (BABC) [ 12 ], the binary gravitational search algorithm (BGSA) [ 13 ], binary grey wolf optimization (BGWO) [ 14 ], the binary salp swarm algorithm (BSSA) [ 15 ], the binary bat algorithm (BBA) [ 16 ], the binary whale optimization algorithm (BWOA) [ 17 ], binary spotted hyena optimization (BSHO) [ 18 ], binary emperor penguin optimization (BEPO) [ 19 ], binary Harris hawks optimization (BHHO) [ 20 ], binary equilibrium optimization (BEO) [ 21 ], binary atom search optimization (BASO) [ 22 ], the binary dragonfly algorithm (BDA) [ 23 ], the binary jaya algorithm (BJA) [ 24 ], binary coronavirus herd immunity optimization (BCHIO) [ 25 ], the binary butterfly optimization algorithm (BBOA) [ 26 ], binary black widow optimization (BBWO) [ 27 ], the binary slime mould algorithm (BSMA) [ 28 ], binary golden eagle optimization (BGEO) [ 29 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection

Li,

Luo,

Zhou

2024

Biomimetics

View full text Add to dashboard Cite

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

show abstract

Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions

Cited by 17 publications

References 232 publications

Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM

Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection

Contact Info

Product

Resources

About