Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous
optimization. For these reasons DE has often been employed for solving various …

Differential evolution (DE) is one of the highly acknowledged population-based optimization
algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems …

Abstract Particle Swarm Optimization (PSO) is among the most universally applied
population-based metaheuristic optimization algorithms. PSO has been successfully used in …

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter
optimization algorithms in current use. DE operates through similar computational steps as …

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm
Optimization and Differential Evolution. Their initial versions were very simple, but rapidly …

Abstract Population size of Differential Evolution (DE) algorithms is often specified by user
and remains fixed during run. During the first decade since the introduction of DE the …

In differential evolution (DE) studies, there are many parameter adaptation methods, aiming
at tuning the mutation factor F and the crossover probability CR. However, these methods …

The existence of the curse of dimensionality is well known, and its general effects are well
acknowledged. However, and perhaps due to this colloquial understanding, specific …

The identification of the current degradation state of an industrial component and the
prediction of its future evolution is a fundamental step for the development of condition …

Differential evolution (DE) is an efficient stochastic algorithm for solving global numerical
optimization problems. To effectively relieve the stagnation and premature convergence …