Bussiness ManagementMBAstrategyHuman ResourceMarketingHospitalityE-commerceInternational Tradingproject managementmedia managementLogisticsFinanceAccountingadvertisingLawBusiness LawEducationEconomicsBusiness Reportbusiness planresearch proposal
英语论文题目英语教学英语论文商务英语英语论文格式商务英语翻译广告英语商务英语商务英语教学英语翻译论文英美文学英语语言学文化交流中西方文化差异英语论文范文英语论文开题报告初中英语教学英语论文文献综述英语论文参考文献
ResumeRecommendation LetterMotivation LetterPSapplication letterMBA essayBusiness Letteradmission letter Offer letter
澳大利亚论文英国论文加拿大论文芬兰论文瑞典论文澳洲论文新西兰论文法国论文香港论文挪威论文美国论文泰国论文马来西亚论文台湾论文新加坡论文荷兰论文南非论文西班牙论文爱尔兰论文
小学英语教学初中英语教学英语语法高中英语教学大学英语教学听力口语英语阅读英语词汇学英语素质教育英语教育毕业英语教学法
英语论文开题报告英语毕业论文写作指导英语论文写作笔记handbook英语论文提纲英语论文参考文献英语论文文献综述Research Proposal代写留学论文代写留学作业代写Essay论文英语摘要英语论文任务书英语论文格式专业名词turnitin抄袭检查
temcet听力雅思考试托福考试GMATGRE职称英语理工卫生职称英语综合职称英语职称英语
经贸英语论文题目旅游英语论文题目大学英语论文题目中学英语论文题目小学英语论文题目英语文学论文题目英语教学论文题目英语语言学论文题目委婉语论文题目商务英语论文题目最新英语论文题目英语翻译论文题目英语跨文化论文题目
日本文学日本语言学商务日语日本历史日本经济怎样写日语论文日语论文写作格式日语教学日本社会文化日语开题报告日语论文选题
职称英语理工完形填空历年试题模拟试题补全短文概括大意词汇指导阅读理解例题习题卫生职称英语词汇指导完形填空概括大意历年试题阅读理解补全短文模拟试题例题习题综合职称英语完形填空历年试题模拟试题例题习题词汇指导阅读理解补全短文概括大意
论文作者:www.51lunwen.org论文属性:课程作业 Coursework登出时间:2016-01-03编辑:zhaotianyun点击率:16964
论文字数:3980论文编号:org201512282037073007语种:英语 English地区:澳门价格:免费论文
摘要:本文主要讲述了共通启发式演算法作为一种优化设计需要各领域多方面的很多搜索。
1.3.2 对共通启发法的分类——1.3.2 Classification of Metaheuristics
This thesis is concerned with metaheuristic algorithms, which are general methods for solving search and optimization problems. Although these algorithms exist today in many different forms, they all search for solutions in essentially the same way. That is, they try out different alternatives, evaluate their worth and then repeat this procedure using what has been learnt to guide them. What differentiates the numerous algorithms, then, is the way that they utilize the information from the solutions they have explored in the past, and that they have in some sense “remembered”, in order to find or construct better solutions in the future.
There are several possible classifications of heuristics and metaheuristics but one that is commonly used and that certainly allows us to embrace most metaheuristics including their hybrids is: single agent search where a single solution is improved incrementally by making small changes to it; and population-based methods where a population of solutions are ‘evolved' in parallel, and improvement results from repeatedly making variations of those solutions in the population that are more highly ‘fit', and discarding those that are less fit (Blum & Roli, 2001).
Examples of single-solution methods are: basic local search (deterministic iterative improvement), simulated annealing, tabu search, greedy randomized adaptive search procedure, variable neighbourhood search, guided local search, iterated local search and others. Population-based methods include: genetic algorithms, scatter search, ant colony systems, memetic algorithms, evolutionary strategies (although some of them are single-agent search), particle swarm systems, cultural algorithms, etc. If a single-agent search approach is hybridised with a population based approach (e.g. a memetic algorithm can be defined to be a genetic algorithm incorporating local search) then the result is, of course, a population-based approach.
Sometimes, researchers classify heuristic and metaheuristic approaches into nature-inspired and non-nature inspired and many refer to the first group as evolutionary algorithms. While these algorithms are commonly conceptualized as those approaches that simulate various aspects of natural evolution (B?ck, Fogel & Michalewicz, 1997), some researchers argue that a fundamental characteristic of evolutionary algorithms is that they handle a 本论文由英语论文网提供整理,提供论文代写,英语论文代写,代写论文,代写英语论文,代写留学生论文,代写英文论文,留学生论文代写相关核心关键词搜索。