such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluat
Over recent years, numerous metaheuristic optimization algorithms (MOAs) have surfaced for addressing the MAED problem. However, none of the literature to date conducted a comprehensive statistical research work on the MAED problem. In part I of this series, we present a comprehensive survey on ...
optimization (SO) (Kenichi and Keiichiro, 2010). In addition to these new metaheuristic algorithms, many other kinds of new metaheuristic algorithms have also been presented after the year 2000. They can be found in several articles andtechnical reports, and even Wikipedia has a page to list ...
Finally, eight complex-valued encoding metaheuristic algorithms were used for 29 benchmark test functions and five engineering optimization design problems. Through the analysis and comparison of the results with statistical significance, the superiority of complex-value encoding was proved, and the ...
Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some common drawback
and hybridization of RS techniques with other optimization algorithms for improved selection are all used32. Since discernibility and dependency minimize dimensionality while keeping significant information, these methods are effective for machine learning feature selection, especially with large and complicated...
A few popular metaheuristic algorithms are included, such as the particle swarm optimization, firefly algorithm, harmony search and others. Cite As Xin-She Yang (2025). Engineering Optimization: An Introduction with Metaheuristic Applications (https://www.mathworks.com/matlabcentral/...
Our goals are to implement all classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do ...
The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization A
(SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluate the potential of a relatively new algorithm and the impact that optimization algorithms can have on the performance of ...