Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed ...

Recent advances in data-driven approaches using the machine learning (ML) method have enabled the discovery of high-performance materials. This paper presents a hybrid framework that combines ML ...

Both algorithms are designed to solve complex optimization problems by mimicking behaviors observed in nature. also it includes a path finding algorithm, A-star python benchmark metaheuristics ...

In most metaheuristic algorithms, the updating process fails to make use of information available from individuals in previous iterations. If this useful information could be exploited fully and used ...

optimization evolutionary-algorithms portfolio-optimization evolutionary-algorithm evolutionary-computation metaheuristic swarm-intelligence continuous-optimization metaheruistics ...

This paper introduces a new metaheuristic algorithm named the Osprey Optimization Algorithm (OOA), which imitates the behavior of osprey in nature. The fundamental inspiration of OOA is the strategy ...

Metaheuristic optimization algorithms are strongly present in the literature on discrete optimization. They typically 1) use stochastic operators, making each run unique, and 2) often have algorithmic ...