首页> 外文OA文献 >Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems
【2h】

Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems

机译:朝着持续优化问题的广义成群质模型

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.
机译:弥撒已成为解决各种实际问题的广泛使用的方法。该文献充满了基于出色的思想的多样性化培育学习,并经过验证的优异能力。尽管如此,常规型遗传案例索取新奇的时候,它们只是重组来自其他方法的元素。因此,对标准的成分型模型的需求对于停止当前基于其鼓舞人心的来源的提出方法的当前狂热趋势至关重要。这项工作介绍了一个普遍和数学上正式的成分型模型的第一步,可用于学习和改进它们。该模型基于简单启发式的方案,其作为构建块执行,可以根据应用程序修改。为此目的,我们定义并详细了解了成阵列(即,其搜索运营商)的所有组件和概念,例如启发式。此外,我们还提供一些想法要考虑到未来探索其他搜索操作员配置。为了说明所提出的模型,我们将搜索运营商分析了在连续优化问题中使用的四个着名的殖民学,作为概念验证。从他们来看,我们派生了20种不同的方法,并使用它们来解决不同的景观的一些基准功能。数据显示了我们对建筑术语和检测到哪些操作员选择的方法的显着能力,根据解决问题。此外,我们概述并讨论该模型的几个未来扩展到各种问题和求解域。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号