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010   $a3-319-58565-7
024 7 $a10.1007/978-3-319-58565-9
035   $a(CKB)3710000001418584
035   $a(MiAaPQ)EBC4898205
035   $a(DE-He213)978-3-319-58565-9
035   $a(PPN)203670027
035   $a(EXLCZ)993710000001418584
100   $a20170703d2017      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $2rdacontent
182   $2rdamedia
183   $2rdacarrier
200 10$aMulti-objective optimization problems $econcepts and self-adaptive parameters with mathematical and engineering applications /$fby Fran Sérgio Lobato, Valder Steffen Jr
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (210 pages) $cillustrations, tables
225 1 $aSpringerBriefs in Mathematics,$x2191-8198
311   $a3-319-58564-9 
320   $aIncludes bibliographical references and index.
327   $aChapter 1 Introduction -- Part 1 Basic Concepts -- Chapter 2 Multi-objective Optimization Problem -- Chapter 3 Treatment of multi-objective Optimization Problem -- Part 2 Methodology -- Chapter 4 Self-Adaptive Multi-objective Optimization Differential Evolution -- Part 3 Applications -- Chapter 5 Mathematical -- Chapter 6 Engineering -- Part 4 Final Considerations -- Chapter 7 Conclusions.
330   $aThis book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
410  0$aSpringerBriefs in Mathematics,$x2191-8198
606   $aMathematical optimization
606   $aEngineering design
606   $aCalculus of variations
606   $aDiscrete Optimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26040
606   $aContinuous Optimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26030
606   $aEngineering Design$3https://scigraph.springernature.com/ontologies/product-market-codes/T17020
606   $aCalculus of Variations and Optimal Control; Optimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26016
615  0$aMathematical optimization.
615  0$aEngineering design.
615  0$aCalculus of variations.
615 14$aDiscrete Optimization.
615 24$aContinuous Optimization.
615 24$aEngineering Design.
615 24$aCalculus of Variations and Optimal Control; Optimization.
676   $a510
700   $aLobato$b Fran Sérgio$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767409
702   $aSteffen Jr$b Valder$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254274403321
996   $aMulti-Objective Optimization Problems$92108118
997   $aUNINA