Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm
motivated by intelligent collective behavior of some animals such as flocks of birds or …

Abstract The “No Free Lunch” theorem states that, averaged over all optimization problems,
without re-sampling, all optimization algorithms perform equally well. Optimization, search …

Several outbreak prediction models for COVID-19 are being used by officials around the
world to make informed decisions and enforce relevant control measures. Among the …

Harnessing the full power of nascent quantum processors requires the efficient management
of a limited number of quantum bits with finite coherent lifetimes. Hybrid algorithms, such as …

This study proposed an improved grasshopper optimization algorithm (GOA) for continuous
optimization and applied it successfully to the financial stress prediction problem. GOA is a …

We introduce Unified Particle Swarm Optimization, a new scheme that harnesses the local
and global variants of the standard Particle Swarm Optimization algorithm, combining their …

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in
optimizing and enhancing efficiency across diverse models and systems, renowned for their …

This paper presents an overview of the research progress in Particle Swarm Optimization
(PSO) during 1995–2017. Fifty two papers have been reviewed. They have been …

This study provides guidelines for the selection of proper goodness-of-fit criteria for the
calibration and evaluation of hydrological models. Popular goodness-of-fit criteria and good …

Many areas in power systems require solving one or more nonlinear optimization problems.
While analytical methods might suffer from slow convergence and the curse of …