Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

The set-based particle swarm optimisation algorithm is a swarm-based meta-heuristic that
has gained popularity in recent years. In contrast to the original particle swarm optimisation …

Hybrid energy systems (HESs) have garnered significant attention as a sustainable solution
to meet the world's growing energy demands while minimizing environmental impact …

The teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based
metaheuristic search algorithm. It combines the exploitation of the teaching learning-based …

Rapid industrialization has fueled the need for effective optimization solutions, which has led
to the widespread use of meta-heuristic algorithms. Among the repertoire of over 600, over …

The quality of operation of neural networks in solving application problems is determined by
the success of the stage of their training. The task of learning neural networks is a complex …

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and
particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze …

A novel algorithm called Poplar Optimization Algorithm (POA) is developed in this paper to
solve continuous optimization problems. The algorithm mimics the sexual and asexual …

The flexible job-shop scheduling problem (FJSP) is a well-known combinational
optimization problem. Studying FJSP is essential for promoting production efficiency and …

Finite-difference methods are a class of algorithms designed to solve black-box optimization
problems by approximating a gradient of the target function on a set of directions. In black …