Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data that makes it ...
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A Metaheuristic Optimization Algorithm refers to a family of methods used in computer science that aim to find a global optimum by repeatedly evaluating the objective function without relying on gradient information. These algorithms, such as evolutionary algorithms, particle swarm optimization, and simula...
The simulation-driven metaheuristic algorithms have been successful in solving numerous problems compared to their deterministic counterparts. Despite this
In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorith
Empty CellCharacteristicsOptimized objectsMetaheuristic optimization algorithms Single-objective Advantages: • Easy to implement • EfficientDisadvantage: • Relatively low ability Combination weights BA [143], B_WCA [132], CLSFPA [33], AFSA-ACO [136], GWO [135] Parameters in predictors BA [83...
Hybrid Metaheuristic Optimization Algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting. Nat Resour Res 33, 1349–1363 (2024). https://doi.org/10.1007/s11053-024-10329-1 Download citation Received04 January 2024 ...
The proposed models give the best results in all inter-electrode spacing distances. Four different metaheuristic optimization algorithms are used in the fitting process to extract all models parameter and find the best optimization algorithm in the bio-impedance problems....
Metaheuristic optimization algorithms have demonstrated their capabilities in finding near-optimal solutions to numerical real-valued problems, for which exact and analytical methods may not be able to produce better solutions within reasonable computation time (Osman and Laporte, 1996, Glover and Kochen...
The novelty of the proposed algorithm was to enhance search performance by making algorithms greedy and using random numbers according to Chaos Theory on the green cloud computing environment. The purpose was to minimize the makespan and cost of performing tasks, to reduce energy consumption to have...