找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Advances in Swarm Intelligence; 15th International C Ying Tan,Yuhui Shi Conference proceedings 2024 The Editor(s) (if applicable) and The A

[复制链接]
楼主: 密度
发表于 2025-3-25 06:00:20 | 显示全部楼层
发表于 2025-3-25 08:23:16 | 显示全部楼层
Gründungsintention von Akademikernoptima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.
发表于 2025-3-25 12:14:12 | 显示全部楼层
Implikationen und Limitationen, The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved Kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.
发表于 2025-3-25 18:25:21 | 显示全部楼层
发表于 2025-3-25 22:23:07 | 显示全部楼层
A Tri-Swarm Particle Swarm Optimization Considering the Cooperation and the Fitness Valuest fitness value were respectively divided into ERS, EIS and CS. The results on seven unimodal benchmark functions demonstrated the superiority of the proposed variant compared with other five variants.
发表于 2025-3-26 01:20:58 | 显示全部楼层
Circle Chaotic Search-Based Butterfly Optimization Algorithmoptima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.
发表于 2025-3-26 05:48:02 | 显示全部楼层
Improved Kepler Optimization Algorithm Based on Mixed Strategy The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved Kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.
发表于 2025-3-26 08:36:38 | 显示全部楼层
发表于 2025-3-26 14:42:36 | 显示全部楼层
发表于 2025-3-26 20:14:32 | 显示全部楼层
Cooperative Search and Rescue Target Assignment Based on Improved Ant Colony Algorithmand makes full use of the global search ability of ant colony algorithm to explore the optimal solution. The simulation results show that this method can quickly and effectively provide the target assignment scheme of search and rescue resources, maximize the survival probability, and improve the efficiency of search and rescue at sea.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-26 05:09
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表