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Bhattacharjya / Bennis

Nature-Inspired Methods for Metaheuristics Optimization

Algorithms and Applications in Science and Engineering

Medium: Buch
ISBN: 978-3-030-26460-4
Verlag: Springer International Publishing
Erscheinungstermin: 26.08.2021
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This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.

Produkteigenschaften

  • Artikelnummer: 9783030264604
  • Medium: Buch
  • ISBN: 978-3-030-26460-4
  • Verlag: Springer International Publishing
  • Erscheinungstermin: 26.08.2021
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2020
  • Serie: Modeling and Optimization in Science and Technologies
  • Produktform: Kartoniert
  • Gewicht: 882 g
  • Seiten: 502
  • Format (B x H x T): 155 x 235 x 26 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

Part I. Algorithms: 1. Genetic algorithms: A mature bio-inspired optimization technique for difficult problems.- 2. Introduction to Genetic Algorithm with a Simple Analogy.- 3. Interactive genetic algorithm to collect user perceptions. Application to the design of stemmed glasses.- 4. Differential Evolution and its application in Identification of Virus Release Location in a Sewer Line.- 5. Arti?cial Bee Colony Algorithm and An Application to Software Defect Prediction.- 6. Firefly Algorithm and its Applications in Engineering Optimization.- 7. Introduction to Shuffled Frog Leaping Algorithm and its Sensitivity to the Parameters of the Algorithm.- 8. Groundwater Management using Coupled Analytic Element based Transient Groundwater Flow and Optimization Model.- 9. Investigation of Bacterial Foraging Algorithm applied for PV parameter estimation, Selective harmonic elimination in inverters and optimal power flow for stability.- 10. Application of artificial immune system in Optimal Design of Irrigation Canal.- 11. Biogeography Based Optimization for Water Pump Switching Problem.- 12. Introduction to Invasive Weed Optimization Method.- 13. Single-Level Production Planning in Petrochemical Industries using Novel Computational Intelligence Algorithms.- 14. A Multi-Agent platform to support knowledge based modelling in engineering Design.- Part II. Applications: 15. Synthesis of reference trajectories for humanoid robot supported by genetic algorithm.- 16. Linked Simulation Optimization Model for Evaluation of Optimal Bank Protection Measures.- 17. A GA Based Iterative Model for Identification of Unknown Groundwater Pollution Sources Considering Noisy Data.- 18. Efficiency of Binary Coded Genetic Algorithm in Stability Analysis of an Earthen Slope.- 19. Corridor allocation as a constrained optimization problem using a permutation-based multi-objective genetic algorithm.- 20. The constrained single-row facility layout problem with repairing mechanisms.- 21.Geometric size optimization of annular step fin array for heat transfer by natural convection.- 22. Optimal control of saltwater intrusion in coastal aquifers using analytical approximation based on density dependent flow correction.- 23. Dynamic Nonlinear Active Noise Control. A Multi-Objective Evolutionary Computing Approach.- 24. Scheduling of Jobs on Dissimilar Parallel Machine using Computational Intelligence Algorithms.- 25. Branch-and-Bound Method for Just-in-Time Optimization of Radar Search Patterns.- 26. Optimization of the GIS based DRASTIC model for Groundwater Vulnerability Assessment.