Differential evolution (DE) is a well-known evolutionary algorithm with simple operation and
excellent performance, which has been applied to solve various optimization problems. To …

This paper presents the study of differential evolution algorithm with hierarchical fair
competition model (HFC-DE). HFC model is based on the fair competition of societal system …

To address complex single objective global optimization problems, a new Level-Based
Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the …

Differential evolution (DE) is a population-based metaheuristic algorithm that has been
proved powerful in solving a wide range of real-parameter optimization tasks. However, the …

A novel self-learning differential evolution (SLDE) algorithm for addressing large-scale
internal tasks scheduling problems in cross-docking is proposed herein. The goal is to …

This study proposed an algorithm called selective strategy differential evolution (SSDE) to
handle the complexity of the stochastic internal task scheduling problem in cross‐docking …

Differential Evolution (DE) is a global optimization process that uses population search to
find the best solution. It offers characteristics such as fast convergence time, simple and …

Differential evolution algorithm (DE) requires diversified population to solve multi-
modalproblems. DE supports non-distributed population. DE versions include differential …

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial
manipulators, this article proposes a method for solving the IK of manipulators using an …

In order to analyze the influence of the objective function on the algorithm, we compare the
common definitions of objective functions and propose a piecewise objective function …