Hybrid Metaheuristics for Multi-Objective Optimization

Hybrid Metaheuristics for Multi-Objective Optimization

Over the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as me...

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Main Author: E-G. Talbi
Format: Article
Language:English
Published: SAGE Publishing 2015-03-01
Series:Journal of Algorithms & Computational Technology
Online Access:https://doi.org/10.1260/1748-3018.9.1.41
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spelling doaj-7c4a270871ba415a9a33e1a67f4aa47a2020-11-25T04:02:42ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262015-03-01910.1260/1748-3018.9.1.41Hybrid Metaheuristics for Multi-Objective OptimizationE-G. TalbiOver the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming and machine learning techniques have provided very powerful search algorithms. Three different types of combinations are considered in this paper to solve multi-objective optimization problems: Combining metaheuristics with (complementary) metaheuristics. Combining metaheuristics with exact methods from mathematical programming approaches. Combining metaheuristics with machine learning and data mining techniques.https://doi.org/10.1260/1748-3018.9.1.41
collection DOAJ
language English
format Article
sources DOAJ
author E-G. Talbi
spellingShingle E-G. Talbi
Hybrid Metaheuristics for Multi-Objective Optimization
Journal of Algorithms & Computational Technology
author_facet E-G. Talbi
author_sort E-G. Talbi
title Hybrid Metaheuristics for Multi-Objective Optimization
title_short Hybrid Metaheuristics for Multi-Objective Optimization
title_full Hybrid Metaheuristics for Multi-Objective Optimization
title_fullStr Hybrid Metaheuristics for Multi-Objective Optimization
title_full_unstemmed Hybrid Metaheuristics for Multi-Objective Optimization
title_sort hybrid metaheuristics for multi-objective optimization
publisher SAGE Publishing
series Journal of Algorithms & Computational Technology
issn 1748-3018
1748-3026
publishDate 2015-03-01
description Over the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming and machine learning techniques have provided very powerful search algorithms. Three different types of combinations are considered in this paper to solve multi-objective optimization problems: Combining metaheuristics with (complementary) metaheuristics. Combining metaheuristics with exact methods from mathematical programming approaches. Combining metaheuristics with machine learning and data mining techniques.
url https://doi.org/10.1260/1748-3018.9.1.41
work_keys_str_mv AT egtalbi hybridmetaheuristicsformultiobjectiveoptimization
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