REVIEW OF MODERN USE OF GENETIC AND EVOLUTIONARY ALGORITHMS. STRATEGIES, POSSIBILITIES (REVIEW ARTICLE)

O. Bondarenko, O. Ustynenko, R. Protasov, I. Klochkov, Borys Vorontsov Borys, Mykola Matyushenko, Pavlo Kalinin
{"title":"REVIEW OF MODERN USE OF GENETIC AND EVOLUTIONARY ALGORITHMS. STRATEGIES, POSSIBILITIES (REVIEW ARTICLE)","authors":"O. Bondarenko, O. Ustynenko, R. Protasov, I. Klochkov, Borys Vorontsov Borys, Mykola Matyushenko, Pavlo Kalinin","doi":"10.20998/2079-0775.2022.2.01","DOIUrl":null,"url":null,"abstract":"Modern trends in the optimal and rational design of technical objects cross a large number of directions of their implementation. One of the interesting and promising directions is genetic and evolutionary algorithms (GА and EA). Authors promote the use of GА and EA as a tool for solving problems of optimal and rational design of complex mechanical systems. The relevance of highlighting modern methods, approaches and strategies for the implementation of GА and EA is described, as well as consideration of their applied implementation, which makes it possible to identify interesting directions of research that, with further adaptation or modifications, can be used to solve the problems of optimal and rational design of gearboxes, boxes gears and transmissions. The main general directions of the literature on GА and EA are highlighted, as well as the practical use of GА and EA in: technical and technological activities, physics, construction, water systems, nanotechnologies, analytical and simulation modeling, electrical and electronic systems, modeling of artificial intelligence and neural networks, information technologies, economic theory, administration and management, marketing, sociology, biology and medicine. This made it possible to understand the course of scientific thought on this issue, to determine the advantages and disadvantages of existing directions and approaches, and helped to choose the vector of further scientific thought, to decide on interesting approaches, strategies and methods. Considering certain features of EA, the authors prefer them. And in terms of strategies, hybridization with other methods, maximum saturation of all stages with \"randomness\" and the possibility of learning (memory organization) of the algorithm similar to neural networks are promising.\nKeywords: optimal design, research directions, genetic algorithms, evolutionary algorithms","PeriodicalId":348363,"journal":{"name":"Bulletin of the National Technical University «KhPI» Series: Engineering and CAD","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of the National Technical University «KhPI» Series: Engineering and CAD","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20998/2079-0775.2022.2.01","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Modern trends in the optimal and rational design of technical objects cross a large number of directions of their implementation. One of the interesting and promising directions is genetic and evolutionary algorithms (GА and EA). Authors promote the use of GА and EA as a tool for solving problems of optimal and rational design of complex mechanical systems. The relevance of highlighting modern methods, approaches and strategies for the implementation of GА and EA is described, as well as consideration of their applied implementation, which makes it possible to identify interesting directions of research that, with further adaptation or modifications, can be used to solve the problems of optimal and rational design of gearboxes, boxes gears and transmissions. The main general directions of the literature on GА and EA are highlighted, as well as the practical use of GА and EA in: technical and technological activities, physics, construction, water systems, nanotechnologies, analytical and simulation modeling, electrical and electronic systems, modeling of artificial intelligence and neural networks, information technologies, economic theory, administration and management, marketing, sociology, biology and medicine. This made it possible to understand the course of scientific thought on this issue, to determine the advantages and disadvantages of existing directions and approaches, and helped to choose the vector of further scientific thought, to decide on interesting approaches, strategies and methods. Considering certain features of EA, the authors prefer them. And in terms of strategies, hybridization with other methods, maximum saturation of all stages with "randomness" and the possibility of learning (memory organization) of the algorithm similar to neural networks are promising. Keywords: optimal design, research directions, genetic algorithms, evolutionary algorithms
回顾遗传和进化算法的现代应用。策略、可能性(评论文章)
技术对象的优化和合理设计的现代趋势跨越了其实施的许多方向。一个有趣和有前途的方向是遗传和进化算法(GА和EA)。作者提倡使用GА和EA作为解决复杂机械系统优化和合理设计问题的工具。重点介绍了实施GА和EA的现代方法、途径和策略的相关性,以及对其应用实施的考虑,这使得有可能确定有趣的研究方向,通过进一步的调整或修改,可以用来解决变速箱、箱齿轮和变速器的优化和合理设计问题。强调了GА和EA文献的主要总体方向,以及GА和EA在以下方面的实际应用:技术和技术活动、物理、建筑、水系统、纳米技术、分析和仿真建模、电气和电子系统、人工智能和神经网络建模、信息技术、经济理论、行政和管理、市场营销、社会学、生物学和医学。这使我们能够了解这个问题的科学思想进程,确定现有方向和方法的优缺点,并有助于选择进一步科学思想的载体,决定有趣的方法、策略和方法。考虑到EA的某些特性,作者更喜欢它们。在策略方面,与其他方法的杂交、各阶段“随机性”的最大饱和以及算法类似神经网络的学习(记忆组织)可能性都是有希望的。关键词:优化设计,研究方向,遗传算法,进化算法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信