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: Evolutionary Multi-Criterion Optimization; Third International Carlos A. Coello Coello,Arturo Hernández Aguirre,E Conference proceedings 2

[復(fù)制鏈接]
樓主: 偏差
51#
發(fā)表于 2025-3-30 10:02:57 | 只看該作者
52#
發(fā)表于 2025-3-30 16:13:29 | 只看該作者
Recombination of Similar Parents in EMO Algorithms flowshop scheduling problems using the NSGA-II algorithm. We focus on the relation between the performance of the NSGA-II algorithm and the similarity of recombined parent solutions. First we show the necessity of crossover operations through computational experiments with various specifications of
53#
發(fā)表于 2025-3-30 19:08:55 | 只看該作者
https://doi.org/10.1007/978-94-017-8709-3imed at improving the speed of convergence beyond a parallel island MOEA with migration. We also suggest a clustering based parallelization scheme for MOEAs and compare it to several alternative MOEA parallelization schemes on multiple standard multi-objective test functions.
54#
發(fā)表于 2025-3-30 22:13:32 | 只看該作者
55#
發(fā)表于 2025-3-31 02:34:57 | 只看該作者
A realistic role for experiment of the Pareto set. Then, we present an original hybridization with Path Relinking algorithm, in order to intensify the search between solutions obtained by the first approach. Results obtained are promising and show that cooperation between these optimization methods could be efficient for Pareto optimization.
56#
發(fā)表于 2025-3-31 07:53:40 | 只看該作者
G. Rossi,G. Madrussani,A. L. Vesnaverg initial populations into existing MOEAs based on so-called Pareto-Front-Arithmetics (PFA). We will provide experimental results from the field of embedded system synthesis that show the effectiveness of our proposed methodology.
57#
發(fā)表于 2025-3-31 12:30:58 | 只看該作者
58#
發(fā)表于 2025-3-31 17:22:58 | 只看該作者
59#
發(fā)表于 2025-3-31 20:45:53 | 只看該作者
An Efficient Multi-objective Evolutionary Algorithm: OMOEA-IIrove the performance in robusticity without degrading precision and distribution of solutions. Experimental results show that OMOEA-II can solve problems with high dimensions and large number of local Pareto-optimal fronts better than some existing algorithms recently reported in the literatures.
60#
發(fā)表于 2025-3-31 22:40:20 | 只看該作者
 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(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, 2025-10-26 22:30
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
乳源| 昭觉县| 商城县| 陵川县| 枞阳县| 信丰县| 宜黄县| 桃园市| 湘阴县| 武清区| 平远县| 永济市| 綦江县| 扬中市| 墨江| 余江县| 蒙自县| 穆棱市| 蒙自县| 南涧| 吕梁市| 郯城县| 宜川县| 甘南县| 蓝山县| 红原县| 玛纳斯县| 绥宁县| 大化| 卢湾区| 平顶山市| 台东县| 海兴县| 黄山市| 安吉县| 莒南县| 宁乡县| 沅江市| 东方市| 佛冈县| 信宜市|