A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends

Metaheuristics has attained increasing interest for solving complex real-world problems. This paper studies the principles and the state-of-the-art of metaheuristic methods for engineering optimization. Both the classic and emerging approaches to optimization using metaheuristics are reviewed and an...

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Main Authors: Ning Xiong, Daniel Molina, Miguel Leon Ortiz, Francisco Herrera
Format: Article
Language:English
Published: Atlantis Press 2015-08-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Online Access:https://www.atlantis-press.com/article/25868618.pdf
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spelling doaj-55f97147aa9f49e589e61e87077d353a2020-11-25T01:38:05ZengAtlantis PressInternational Journal of Computational Intelligence Systems 1875-68832015-08-018410.1080/18756891.2015.1046324A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent TrendsNing XiongDaniel MolinaMiguel Leon OrtizFrancisco HerreraMetaheuristics has attained increasing interest for solving complex real-world problems. This paper studies the principles and the state-of-the-art of metaheuristic methods for engineering optimization. Both the classic and emerging approaches to optimization using metaheuristics are reviewed and analyzed. All the methods are discussed in three basic types: trajectory-based, in which in each step a new solution is created from the previous one; multi-trajectory-based, in which a multi-start mechanism is used; and population-based, where multiple new solutions are created considering a population of approximate solutions. We further discuss algorithms and strategies to handle multi-modal and multi-objective optimization tasks as well as methods for parallel implementation of metaheuristic algorithms. Then, different software frameworks for metaheuristics are introduced. Finally, several interesting directions are pointed out as future research trends.https://www.atlantis-press.com/article/25868618.pdfmetaheuristicsoptimization methodstrajectory-based optimizationpopulation-based optimizationmultimodal optimizationmulti-objective optimization
collection DOAJ
language English
format Article
sources DOAJ
author Ning Xiong
Daniel Molina
Miguel Leon Ortiz
Francisco Herrera
spellingShingle Ning Xiong
Daniel Molina
Miguel Leon Ortiz
Francisco Herrera
A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
International Journal of Computational Intelligence Systems
metaheuristics
optimization methods
trajectory-based optimization
population-based optimization
multimodal optimization
multi-objective optimization
author_facet Ning Xiong
Daniel Molina
Miguel Leon Ortiz
Francisco Herrera
author_sort Ning Xiong
title A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
title_short A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
title_full A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
title_fullStr A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
title_full_unstemmed A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
title_sort walk into metaheuristics for engineering optimization: principles, methods and recent trends
publisher Atlantis Press
series International Journal of Computational Intelligence Systems
issn 1875-6883
publishDate 2015-08-01
description Metaheuristics has attained increasing interest for solving complex real-world problems. This paper studies the principles and the state-of-the-art of metaheuristic methods for engineering optimization. Both the classic and emerging approaches to optimization using metaheuristics are reviewed and analyzed. All the methods are discussed in three basic types: trajectory-based, in which in each step a new solution is created from the previous one; multi-trajectory-based, in which a multi-start mechanism is used; and population-based, where multiple new solutions are created considering a population of approximate solutions. We further discuss algorithms and strategies to handle multi-modal and multi-objective optimization tasks as well as methods for parallel implementation of metaheuristic algorithms. Then, different software frameworks for metaheuristics are introduced. Finally, several interesting directions are pointed out as future research trends.
topic metaheuristics
optimization methods
trajectory-based optimization
population-based optimization
multimodal optimization
multi-objective optimization
url https://www.atlantis-press.com/article/25868618.pdf
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