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Constraint Handling in Cohort Intelligence Algorithm

Medium: Buch
ISBN: 978-1-032-15075-8
Verlag: CRC Press
Erscheinungstermin: 27.12.2021
Lieferfrist: bis zu 10 Tage

Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms.

Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined.

Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.


Produkteigenschaften


  • Artikelnummer: 9781032150758
  • Medium: Buch
  • ISBN: 978-1-032-15075-8
  • Verlag: CRC Press
  • Erscheinungstermin: 27.12.2021
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2021
  • Serie: Advances in Metaheuristics
  • Produktform: Gebunden, HC gerader Rücken kaschiert
  • Gewicht: 482 g
  • Seiten: 206
  • Format (B x H x T): 161 x 240 x 16 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Chapter 1: Introduction to Metaheuristic AlgorithmsChapter 2: Literature Survey on Nature Inspired Optimisation Methodologies and Constraint HandlingChapter 3: Cohort Intelligence (CI) Using the Static Penalty Function (SPF) ApproachChapter 4: Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) ApproachChapter 5: Hybridization of Cohort Intelligence with Colliding Bodies OptimisationChapter 6: Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable ProblemsChapter 7: Solution to Real-World ApplicationsChapter 8: Conclusions and RecommendationsAppendix: Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing ProblemsIndex