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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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 |
work_keys_str_mv |
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