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) and particle swarm optimization (PSO) are two formidable
population-based optimizers (POs) that follow different philosophies and paradigms, which …

Metaheuristics are widely recognized as efficient approaches for many hard optimization
problems. This paper provides a survey of some of the main metaheuristics. It outlines the …

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 …

Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization.
It has been widely used in many areas. Biogeography-based optimization (BBO) is a new …

The performance of the differential evolution (DE) algorithm is significantly affected by the
choice of mutation strategies and control parameters. Maintaining the search capability of …

Many real-world optimization problems are large-scale in nature. In order to solve these
problems, an optimization algorithm is required that is able to apply a global search …

This paper presents insight into an adaptation and self-adaptation mechanism within
differential evolution, covering not only how but moreover–when this mechanism generates …

The artificial bee colony (ABC) algorithm is a new branch of evolutionary algorithms (EAs)
that is inspired by the collective foraging behavior of real honey bee colonies. Due to its …

A new mechanism for the adaptation of the population size in differential evolution (DE) is
described. The adaptive mechanism is based on linear reduction of the population diversity …