Differential evolution (DE) is a simple, yet very effective, population-based search technique.
However, it is challenging to maintain a balance between exploration and exploitation …

Hybridization with other different algorithms is an interesting direction for the improvement of
differential evolution (DE). In this paper, a hybrid DE based on the one-step k-means …

Differential evolution (DE) is an efficient population-based search algorithm with good
robustness, but it faces challenges in dealing with Large-Scale Global Optimization (LSGO) …

Differential evolution (DE) is a simple and efficient global optimization algorithm. However,
DE has been shown to have certain weaknesses, especially if the global optimum should be …

This paper presents Differential Evolution algorithm for solving high‐dimensional
optimization problems over continuous space. The proposed algorithm, namely, ANDE …

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 …

Differential evolution (DE) is one competitive form of evolutionary algorithms. It heavily relies
on mutating solutions using scaled differences of randomly selected individuals from the …

We propose a crossover-based adaptive local search (LS) operation for enhancing the
performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is …

Differential evolution (DE) is a simple and effective global optimization algorithm. It has been
successfully applied to solve a wide range of real-world optimization problems. However …

Differential evolution (DE) is one of the most successful evolutionary algorithms (EAs) for
global numerical optimization. Like other EAs, maintaining population diversity is important …