找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

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

[復(fù)制鏈接]
樓主: 密度
21#
發(fā)表于 2025-3-25 06:00:20 | 只看該作者
22#
發(fā)表于 2025-3-25 08:23:16 | 只看該作者
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.
23#
發(fā)表于 2025-3-25 12:14:12 | 只看該作者
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.
24#
發(fā)表于 2025-3-25 18:25:21 | 只看該作者
25#
發(fā)表于 2025-3-25 22:23:07 | 只看該作者
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.
26#
發(fā)表于 2025-3-26 01:20:58 | 只看該作者
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.
27#
發(fā)表于 2025-3-26 05:48:02 | 只看該作者
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.
28#
發(fā)表于 2025-3-26 08:36:38 | 只看該作者
29#
發(fā)表于 2025-3-26 14:42:36 | 只看該作者
30#
發(fā)表于 2025-3-26 20:14:32 | 只看該作者
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.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-12 15:59
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
绥滨县| 东丰县| 沙湾县| 仪陇县| 招远市| 谷城县| 泾川县| 连平县| 兴山县| 玉溪市| 双鸭山市| 梅河口市| 桑日县| 木里| 平度市| 囊谦县| 原阳县| 新民市| 拉萨市| 乡宁县| 施秉县| 芦山县| 萝北县| 新郑市| 洛阳市| 福建省| 翁源县| 屯昌县| 台北市| 敖汉旗| 巴林右旗| 积石山| 太湖县| 宣威市| 鄂托克前旗| 道孚县| 三亚市| 新化县| 晴隆县| 监利县| 隆安县|