Over the past few years, differential evolution (DE) is generally considered as a reliable,
accurate and robust population-based evolutionary algorithm (EA). It is capable of handling …

Abstract The Differential Evolution (DE) algorithm is one of the most popular and studied
approaches in Evolutionary Computation (EC). Its simple but efficient design, such as its …

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 reliable and versatile function optimiser especially suited for
continuous optimisation problems. Practical experience, however, shows that DE easily …

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 …

Differential evolution (DE) is a population-based meta-heuristic technique for global
optimization capable of handling non-differentiable, nonlinear, and multi-modal objective …

Differential evolution (DE) is one of the most influential optimization algorithms up-to-date.
DE works through analogous computational steps as used by a standard evolutionary …

The performance of most metaheuristic algorithms depends on parameters whose settings
essentially serve as a key function in determining the quality of the solution and the …

Over the last few decades, a number of differential evolution (DE) algorithms have been
proposed with excellent performance on mathematical benchmarks. However, like any other …

Due to its simple algorithm structure, ease of performance, high optimization efficiency,
simple parameter setting, and excellent robustness, the differential evolution (DE) algorithm …