LEADER 02672nam 2200505 450 001 9910814759803321 005 20230807211518.0 010 $a92-64-24839-0 035 $a(CKB)3710000000608185 035 $a(WaSeSS)IndRDA00109668 035 $a(MiAaPQ)EBC4961778 035 $a(MiAaPQ)EBC6412466 035 $a(Au-PeEL)EBL4961778 035 $a(CaONFJC)MIL883577 035 $a(OCoLC)1024276159 035 $a(FR-PaOEC)9789264248397-fr 035 $a(Au-PeEL)EBL6412466 035 $a(OCoLC)1225548112 035 $a(EXLCZ)993710000000608185 100 $a20220526d2015 uy 0 101 0 $afre 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aLa croissance verte dans les pe?ches et l'aquaculture /$fOCDE 210 1$aParis :$cOCDE,$d[2015] 210 4$d©2015 215 $a1 online resource (124 pages) 225 0 $aE?tudes de l'OCDE sur la croissance verte 311 $a92-64-24838-2 320 $aIncludes bibliographical references. 327 $aLa croissance verte dans l'aquaculture -- Abréviations -- Avant-propos et remerciements -- La croissance verte dans l'économie bleue : intégrer pêche, aquaculture et environnement -- La croissance verte dans les pêches -- Résumé. 330 3 $aCe rapport résume l?état des pêcheries et de l?aquaculture et note que dans de nombreuses régions du monde, ces secteurs sont menacés et n?atteignent pas pleinement leur potentiel. Une croissance soutenue pourrait néanmoins être atteinte si des réformes sont engagées dans la lignée de la Stratégie de croissance verte de l?OCDE. Le rapport souligne le besoin d?une gestion des stocks sérieuse et basée sur les connaissances scientifiques pour garantir la durabilité des ressources ainsi que d?un cycle de développement des politiques transparent et dynamique qui garantisse que les pêcheries génèrent le maximum de bénéfices possibles. Le rapport montre qu?une amélioration de la régulation en termes d?externalités environnementales et de compétition spatiale pour l?exploitation des ressources est un élément clé qui permettrait d?ouvrir des perspectives de croissance pour l?aquaculture. 410 0$aÉtudes de l'OCDE sur la croissance verte,$x2222954X. 606 $aFisheries$xEnvironmental aspects 615 0$aFisheries$xEnvironmental aspects. 676 $a577.727 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814759803321 996 $aLa croissance verte dans les pe?ches et l'aquaculture$94105017 997 $aUNINA LEADER 05466nam 22005655 450 001 9910484658003321 005 20200703182341.0 010 $a3-319-96433-X 024 7 $a10.1007/978-3-319-96433-1 035 $a(CKB)4100000006674677 035 $a(MiAaPQ)EBC5526670 035 $a(DE-He213)978-3-319-96433-1 035 $a(PPN)243771576 035 $a(EXLCZ)994100000006674677 100 $a20181109d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Intelligence, Optimization and Inverse Problems with Applications in Engineering /$fedited by Gustavo Mendes Platt, Xin-She Yang, Antônio José Silva Neto 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (301 pages) 311 $a3-319-96432-1 320 $aIncludes bibliographical references and index. 327 $aChapter 01- An Overview of the Use of Metaheuristics in Two Phase Equilibrium Calculation Problems -- Chapter 02- Reliability-based Robust Optimization Applied to Engineering System Design -- Chapter 03- On Initial Populations of Differential Evolution for Practical Optimization Problems -- Chapter 04- Application of Enhanced Particle Swarm Optimization in Euclidean Steiner Tree Problem Solving in RN -- Chapter 05- Rotation-Based Multi-Particle Collision Algorithm with Hooke-Jeeves Approach Applied to the Structural Damage Identification -- Chapter 06- Optimization in Civil Engineering and Metaheuristic Algorithms: a Review of State-of-the-Art Developments -- Chapter 07- A Bioreactor Fault Diagnosis Based on Metaheuristics -- Chapter 08- Optimization of Nuclear Reactors Loading Patterns with Computational Intelligence Methods -- Chapter 09- Inverse Problem of an Anomalous Diffusion Model Employing Lightning Optimization Algorithm -- Chapter 10- Study of the Impact of the Topology of Artificial Neural Networks for the Prediction of Meteorological Data -- Chapter 11- Constructal Design Associated with Genetic Algorithm to Maximize the Performance of H-shaped Isothermal Cavities -- Chapter 12- Co-Design System for Tracking Targets using Template Matching -- Chapter 13- A Hybrid Estimation Scheme Based on the Sequential Importance Resampling Particle Filter and the Particle Swarm Optimization (PSO-SIR) -- Chapter 14- Fault Detection Using Kernel Computational Intelligence Algorithms -- Index. 330 $aThis book focuses on metaheuristic methods and its applications to real-world problems in Engineering. The first part describes some key metaheuristic methods, such as Bat Algorithms, Particle Swarm Optimization, Differential Evolution, and Particle Collision Algorithms. Improved versions of these methods and strategies for parameter tuning are also presented, both of which are essential for the practical use of these important computational tools. The second part then applies metaheuristics to problems, mainly in Civil, Mechanical, Chemical, Electrical, and Nuclear Engineering. Other methods, such as the Flower Pollination Algorithm, Symbiotic Organisms Search, Cross-Entropy Algorithm, Artificial Bee Colonies, Population-Based Incremental Learning, Cuckoo Search, and Genetic Algorithms, are also presented. The book is rounded out by recently developed strategies, or hybrid improved versions of existing methods, such as the Lightning Optimization Algorithm, Differential Evolution with Particle Collisions, and Ant Colony Optimization with Dispersion ? state-of-the-art approaches for the application of computational intelligence to engineering problems. The wide variety of methods and applications, as well as the original results to problems of practical engineering interest, represent the primary differentiation and distinctive quality of this book. Furthermore, it gathers contributions by authors from seven countries ? some of which are the original proponents of the methods presented ? and 21 research centers around the globe. 606 $aComputational intelligence 606 $aMathematical optimization 606 $aProbabilities 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 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 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 615 0$aComputational intelligence. 615 0$aMathematical optimization. 615 0$aProbabilities. 615 14$aComputational Intelligence. 615 24$aDiscrete Optimization. 615 24$aContinuous Optimization. 615 24$aProbability Theory and Stochastic Processes. 676 $a006.3019 702 $aPlatt$b Gustavo Mendes$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYang$b Xin-She$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSilva Neto$b Antônio José$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910484658003321 996 $aComputational Intelligence, Optimization and Inverse Problems with Applications in Engineering$92852811 997 $aUNINA