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 …

The increasing complexity of real-world optimization problems raises new challenges to
evolutionary computation. Responding to these challenges, distributed evolutionary …

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

Due to the increasing complexity of optimization problems, distributed differential evolution
(DDE) has become a promising approach for global optimization. However, similar to the …

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter
optimization algorithms in current use. DE operates through similar computational steps as …

Comparing, or benchmarking, of optimization algorithms is a complicated task that involves
many subtle considerations to yield a fair and unbiased evaluation. In this paper, we …

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

In electromagnetics, optimization problems generally require high computational resources
and involve a large number of unknowns. They are usually characterized by non-convex …

In this paper, a neighborhood mutation strategy is proposed and integrated with various
niching differential evolution (DE) algorithms to solve multimodal optimization problems …

This paper presents an evolutionary-based approach to solve the optimal power flow (OPF)
problem. The proposed approach employs differential evolution (DE) algorithm for optimal …