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 ...
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 evaluate the potential of a relatively new algorithm and...
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 ...
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
(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 ...
pyMetaheuristicis a robust Python Library crafted to provide a wide range of metaheuristic algorithms, ideal for tackling complex optimization tasks. It encompasses a diverse mix of algorithms, from traditional to modern methods. For a detailed list of these metaheuristics and their demonstrations, refe...
MEALPY is the largest python library in the world for most of the cutting-edge meta-heuristic algorithms (nature-inspired algorithms, black-box optimization, global search optimizers, iterative learning algorithms, continuous optimization, derivative free optimization, gradient free optimization, zeroth ord...
(NFL) principle10, these metaheuristic algorithms cannot obtain global optimal solutions for all optimization problems. Inspired by the NFL principle, this study proposes a new algorithm named “Aphid Optimization Algorithms (AOA)” to broaden the investigation of metaheuristic optimization algorithms. ...
Metaheuristic algorithms are one of the most effective stochastic approaches to solve optimization problems. Metaheuristic algorithms are able to provide suitable solutions for optimization problems without the need for a derivation process, based on random search in the problem-solving space and using ...
Finally, it is very important to highlight here that because the choice of parameter settings can significantly affect the quality of solutions generated by the individual metaheuristic optimization algorithms, several experiments need to be performed in order to find the best combination of parameters ...