<span>The purpose of this systematic literature review (SLR) article is to discuss the findings state-of-art metaheuristic nature-inspired algorithm (MHNIA) in reservoir optimization operation. The rationale approach elucidate optimal way as decision making that implemented MHNIA for several complex problems Commonly, has always been used hydrology field, especially optimization. Hence, p...

In the era of healthcare and its related research fields, dimensionality problem high-dimensional data is a massive challenge as it crucial to identify significant genes while conducting on diseases like cancer. As result, studying new Machine Learning (ML) techniques for raw gene expression biomedical an important field research. Disease detection, sample classification, early disease predicti...

We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well path relinking for refinement. The core optimization module consists of a biased genetic algorithm. Through distinct separation problem-independent problem-dependent modules, we achieve an efficien...

In recent years, several high-performance nature-inspired metaheuristic algorithms have been proposed. It is important to study and compare the convergence, computational burden statistical significance of these metaheuristics aid future developments. This focuses on six metaheuristics, namely, ant lion optimization (ALO), arithmetic algorithm (AOA), dragonfly (DA), grey wolf optimizer (GWO), s...

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales’ behavior of communicating with each other via ultras...