valentinosunaenciso / 2022stirmegicdifferentialevolution

Experiments from the paper "A Stigmergy-Based Differential Evolution", published in 2022.

2022aStirmegyDifferentialEvolution

Abstract: Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far from the whole population to guide the search process. Although this approach has advantages in the algorithm’s exploitation process, it is not completely in agreement with the swarms found in nature, where communication among individuals is not centralized. This paper proposes the use of stigmergy as an inspiration to modify the original DE operators to simulate a decentralized information exchange, thus avoiding the application of a global best. The Stigmergy-based DE (SDE) approach was tested on a set of benchmark problems to compare its performance with DE. Even though the execution times of DE and SDE are very similar, our proposal has a slight advantage in most of the functions and can converge in fewer iterations in some cases, but its main feature is the capability to maintain a good convergence behavior as the dimensionality grows, so it can be a good alternative to solve complex problems.

This repository has some of the experiments performed in the paper "A Stigmergy-Based Differential Evolution". The original code was programmed using CUDA-C Apis, and each experiment is fully contained in each folder, which are numerated. We made the programs in Visual Studio 2019. Be aware that several configurations must be made to your particular Nvidia card before trying to run every code. If you utilize all or part of this repository, please cite us as: "Osuna-Enciso, V., & Guevara-Martínez, E. (2022). A Stigmergy-Based Differential Evolution. Applied Sciences, 12(12), 6093."