Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary
optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed …

Over the last few decades, many population-based swarm and evolutionary algorithms were
introduced in the literature. It is well known that population topology or sociometry plays an …

Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple
populations for cooperatively solving complex optimization problems. However, how to …

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

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of
current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent …

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 …

This paper presents enhanced fitness-adaptive differential evolution algorithm with novel
mutation (EFADE) for solving global numerical optimization problems over continuous …

Differential evolution (DE) is a class of simple yet powerful evolutionary algorithms for global
numerical optimization. Binomial crossover and exponential crossover are two commonly …

An optimization spiking neural P system (OSNPS) aims to obtain the approximate solutions
of combinatorial optimization problems without the aid of evolutionary operators of …

Existing differential evolution (DE) algorithms often face two challenges. The first is that the
optimization performance is significantly affected by the ad hoc configurations of operators …