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[null,"1.","Record Nr.",null,"UNINA9910254274403321"]
[null,null,"Autore",null,"Lobato Fran Sérgio"]
[null,null,"Titolo",null,"Multi-objective optimization problems : concepts and self-adaptive parameters with mathematical and engineering applications / / by Fran Sérgio Lobato, Valder Steffen Jr"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017"]
[null,null,"ISBN",null,"3-319-58565-7"]
[null,null,"Edizione",null,"[1st ed. 2017.]"]
[null,null,"Descrizione fisica",null,"1 online resource (210 pages) : illustrations, tables"]
[null,null,"Collana",null,"SpringerBriefs in Mathematics, , 2191-8198"]
[null,null,"Disciplina",null,"510"]
[null,null,"Soggetti",null,"Mathematical optimization"]
[{"$nulls":4},"Engineering design"]
[{"$nulls":4},"Calculus of variations"]
[{"$nulls":4},"Discrete Optimization"]
[{"$nulls":4},"Continuous Optimization"]
[{"$nulls":4},"Engineering Design"]
[{"$nulls":4},"Calculus of Variations and Optimal Control; Optimization"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Chapter 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."]
[null,null,null,"Sommario/riassunto","This 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 "]
[{"$nulls":4},"beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others."]