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200 10$aMulti-Objective Optimization using Artificial Intelligence Techniques /$fby Seyedali Mirjalili, Jin Song Dong
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (xi, 58 pages)
225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712
311 08$a3-030-24834-8 
330   $aThis book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.
410  0$aSpringerBriefs in Computational Intelligence,$x2625-3712
606   $aComputational intelligence
606   $aMachine learning
606   $aOperations research
606   $aComputational Intelligence
606   $aMachine Learning
606   $aOperations Research and Decision Theory
615  0$aComputational intelligence.
615  0$aMachine learning.
615  0$aOperations research.
615 14$aComputational Intelligence.
615 24$aMachine Learning.
615 24$aOperations Research and Decision Theory.
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702   $aDong$b Jin Song$4aut$4http://id.loc.gov/vocabulary/relators/aut
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