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 …

Although various population initialization techniques have been employed in evolutionary
algorithms (EAs), there lacks a comprehensive survey on this research topic. To fill this gap …

In classification, feature selection is an important data pre-processing technique, but it is a
difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is …

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 …

Most of the existing works on reliability–redundancy allocation problem (RRAP) focus on
source-to-sink systems. This work formulates the RRAP with global reliability (g-reliability) …

Metaheuristic algorithms have been widely used to solve diverse kinds of optimization
problems. For an optimization problem, population initialization plays a significant role in …

A situation where the set of initial solutions lies near the position of the true optimality (most
favourable or desirable solution) by chance can increase the probability of finding the true …

Accurate electrical load forecasting always plays a vital role in power system administration
and energy dispatch, which are the foundation of the smooth operation of the national …

Particle swarm optimization (PSO) algorithm is a population‐based intelligent stochastic
search technique used to search for food with the intrinsic manner of bee swarming. PSO is …

Large-scale optimization, solving real high-dimensional problems, has attracted many
research interests. Large-scale optimization problems are far more difficult than traditional …