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
梁河县| 天门市| 陆川县| 遂川县| 会同县| 新宾| 津市市| 马鞍山市| 罗田县| 祁东县| 西吉县| 梅河口市| 田林县| 莱西市| 湖南省| 秦皇岛市| 凤冈县| 杨浦区| 江山市| 年辖:市辖区| 亳州市| 富民县| 曲水县| 北辰区| 大关县| 资阳市| 厦门市| 微山县| 拜泉县| 肃北| 七台河市| 林口县| 乌兰县| 青田县| 五峰| 布拖县| 阳曲县| 甘南县| 崇阳县| 黑山县| 呼玛县|