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

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with
nonlinear and complex problems. However, these population-based algorithms are …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and
various operators in order to address optimization problems. The combination and …

Previous studies have shown that differential evolution is an efficient, effective and robust
evolutionary optimization method. However, the convergence rate of differential evolution in …

In this paper, we introduce ACO MV: an ant colony optimization (ACO) algorithm that
extends the ACO R algorithm for continuous optimization to tackle mixed-variable …

This chapter discuss basic principles of Self-Organizing Migrating Algorithm (SOMA) that
has been firstly proposed in 1999 and published consequently in various journals, book …

This article discusses the stagnation of an evolutionary optimization algorithm called
Differential Evolution. Stagnation problem refers to a situation in which the optimum seeking …

An extension for the differential evolution algorithm is proposed for handling nonlinear
constraint functions. In comparison with the original algorithm, only the replacement criterion …