Particle swarm optimization (PSO), like other population-based meta-heuristics, is
intrinsically parallel and can be effectively implemented on Graphics Processing Units …

This work presents and illustrates the application of hydroPSO, a novel multi-OS and model-
independent R package used for model calibration. hydroPSO allows the modeller to …

Particle swarm optimization (PSO) is a population-based metaheuristic optimization
algorithm that solves a problem through iterative operations. Traditional PSO iteration …

Several interesting libraries for optimization have been proposed. Some focus on individual
optimization algorithms, or limited sets of them, and others focus on limited sets of problems …

In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and
positions are updated after the whole swarm performance is evaluated. This algorithm is …

In this work, we demonstrate the superior exploration capabilities of the population-based
methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to …

Optimisation meta-heuristics such as particle swarm optimisation (PSO) require high-
performance computing (HPC). The use of software parallelism and hardware parallelism is …

Soft computing (SC) includes computational techniques that are tolerant of approximations,
missing information, and uncertainty, and aim at providing effective and efficient solutions to …

Optimization problems are frequently found in various fields. The classification of estimation-
based metaheuristic algorithms has been introduced for solving optimization problems …

Soft computing (SC) includes computational techniques that are tolerant of approximations,
missing information, and uncertainty, and aim at providing effective and efficient solutions to …