找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Advances in Swarm Intelligence; 7th International Co Ying Tan,Yuhui Shi,Li Li Conference proceedings 2016 Springer International Publishing

[復(fù)制鏈接]
樓主: irritants
31#
發(fā)表于 2025-3-26 21:37:27 | 只看該作者
32#
發(fā)表于 2025-3-27 01:57:22 | 只看該作者
On-Orbit Servicing Mission Planning for Multi-spacecraft Using CDPSO optimization (CDPSO) algorithm is applied according to the characteristics of multi-spacecraft collaborative mission planning problem. We design the new update formulae of position and velocity of the particles for the OOS optimization mission. By analyzing the critical index factors which contain
33#
發(fā)表于 2025-3-27 05:31:39 | 只看該作者
Solving the Test Task Scheduling Problem with a Genetic Algorithm Based on the Scheme Choice Rulech combines a genetic algorithm with a new rule for scheme selection is adopted to find optimal solutions. GASCR is a hierarchal approach based on the characteristics of TTSP because the given problem can be decomposed into task sequence and scheme choice. GA with the non-Abelian (Nabel) crossover a
34#
發(fā)表于 2025-3-27 11:08:50 | 只看該作者
35#
發(fā)表于 2025-3-27 17:22:52 | 只看該作者
36#
發(fā)表于 2025-3-27 20:35:17 | 只看該作者
Solving Flexible Job-Shop Scheduling Problem with Transfer Batches, Setup Times and Multiple Resourcducts. However, Flexible Job-shop Scheduling is really challenging and even more complex when setup times, transfer batches and multiple resources are added. In this paper, we present an application of dispatching algorithm for the Flexible Job-shop Scheduling Problem (FJSP) presented in this indust
37#
發(fā)表于 2025-3-27 23:46:48 | 只看該作者
38#
發(fā)表于 2025-3-28 05:31:10 | 只看該作者
39#
發(fā)表于 2025-3-28 08:17:07 | 只看該作者
An Improved Ensemble Extreme Learning Machine Based on ARPSO and Tournament-Selectionperformance and simple setting. However, how to select and cluster the candidate are still the most important issues. In this paper, KGA-ARPSOELM, an improved ensemble of ELMs based on K-means, tournament-selection and attractive and repulsive particle swarm optimization (ARPSO) strategy is proposed
40#
發(fā)表于 2025-3-28 14:30:32 | 只看該作者
An Improved LMDS AlgorithmS (LMDS) is a fast algorithm of CMDS. In LMDS, some data points are designated as landmark points. When the intrinsic dimension of the landmark points is less than the intrinsic dimension of the data set, the embedding recovered by LMDS is not consistent with that of classical multidimensional scali
 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(ài)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(píng) 投稿經(jīng)驗(yàn)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(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-8 18:59
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
洱源县| 呼和浩特市| 八宿县| 邮箱| 张家界市| 克东县| 慈利县| 安福县| 霞浦县| 柏乡县| 都匀市| 谷城县| 河北区| 息烽县| 永吉县| 西盟| 志丹县| 孝昌县| 读书| 新田县| 台湾省| 虞城县| 德令哈市| 灌阳县| 抚顺市| 筠连县| 苏尼特左旗| 桦甸市| 宝坻区| 新邵县| 南丹县| 武汉市| 绥芬河市| 金阳县| 宁夏| 株洲市| 同江市| 沅江市| 乌拉特前旗| 上饶市| 龙游县|