一种用于稳健高效高维搜索的增强型面向团队的群体优化算法（ETOSO）

An Enhanced Team-Oriented Swarm Optimization Algorithm (ETOSO) for Robust and Efficient High-Dimensional Search.

作者信息

BenAbdennour Adel

机构信息

College of Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia.

出版信息

Biomimetics (Basel). 2025 Apr 3;10(4):222. doi: 10.3390/biomimetics10040222.

DOI:10.3390/biomimetics10040222

PMID:40277621

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12025233/

Abstract

This paper introduces the Enhanced Team-Oriented Swarm Optimization (ETOSO) algorithm, a novel refinement of the Team-Oriented Swarm Optimization (TOSO) algorithm aimed at addressing the stagnation problem commonly encountered in nature-inspired optimization approaches. ETOSO enhances TOSO by integrating innovative strategies for exploration and exploitation, resulting in a simplified algorithm that demonstrates superior performance across a broad spectrum of benchmark functions, particularly in high-dimensional search spaces. A comprehensive comparative evaluation and statistical tests against 26 established nature-inspired optimization algorithms (NIOAs) across 15 benchmark functions and dimensions (D = 2, 5, 10, 30, 50, 100, 200) confirm ETOSO's superiority relative to solution accuracy, convergence speed, computational complexity, and consistency.

摘要

本文介绍了增强型面向团队的群体优化（ETOSO）算法，它是面向团队的群体优化（TOSO）算法的一种新颖改进，旨在解决自然启发式优化方法中常见的停滞问题。ETOSO通过集成探索和利用的创新策略来增强TOSO，从而得到一种简化算法，该算法在广泛的基准函数中表现出卓越性能，尤其是在高维搜索空间中。针对15个基准函数和维度（D = 2、5、10、30、50、100、200）上的26种既定自然启发式优化算法（NIOAs）进行的全面比较评估和统计测试证实了ETOSO在解的准确性、收敛速度、计算复杂性和一致性方面的优越性。