Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most
frequently used algorithms for solving complex optimization problems. Its flexibility and …

Differential evolution is an effective and practical approach that is widely applied for solving
global optimization problems. Nevertheless, its effectiveness and scalability are decreased …

The two major groups representing biologically inspired algorithms are swarm intelligence
(SI) and evolutionary computation (EC). Both SI and EC share common features such as the …

Differential evolution (DE) is among the most efficient evolutionary algorithms (EAs) for
global optimization and now widely applied to solve diverse real-world applications. As the …

In population-based optimization algorithms (POAs), given an optimization problem, the
quality of the solutions depends heavily on the selection of algorithms, strategies and …

Trial vector generation strategies and control parameters have a significant influence on the
performance of differential evolution (DE). This paper studies whether the performance of …

This is the second edition of our 2003 book. It is primarily a book for lecturers and graduate
and undergraduate students. To this group the book offers a thorough introduction to …

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 …

In this paper we present a conceptual framework for parameter tuning, provide a survey of
tuning methods, and discuss related methodological issues. The framework is based on a …

An effective and efficient self-adaptation framework is proposed to improve the performance
of the L-SHADE algorithm by providing successful alternative adaptation for the selection of …