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Record Nr. |
UNINA9910484985003321 |
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Autore |
Mirjalili Seyedali |
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Titolo |
Multi-Objective Optimization using Artificial Intelligence Techniques / / by Seyedali Mirjalili, Jin Song Dong |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (xi, 58 pages) |
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Collana |
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SpringerBriefs in Computational Intelligence, , 2625-3712 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Operations research |
Computational Intelligence |
Machine Learning |
Operations Research and Decision Theory |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage. |
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