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Constraint Handling in Cohort Intelligence Algorithm - Ishaan R. Kale, Anand J. Kulkarni

Constraint Handling in Cohort Intelligence Algorithm

Buch | Softcover
200 Seiten
2024
CRC Press (Verlag)
978-1-032-15657-6 (ISBN)
CHF 78,50 inkl. MwSt
This is a valuable reference to practitioners, students and researchers in the area of optimization methods. CI is investigated by solving discrete variable truss structural problems, mixed variable design engineering problems, linear and nonlinear constrained test problems and real-world applications from the manufacturing domain.
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.

Ishaan R. Kale is a researcher for the Optimization and Agent Technology Research (OAT Research) Lab. Anand J. Kulkarni is an Associate Professor at the Institute of Artificial Intelligence, MIT World Peace University, India.

Chapter 1: Introduction to Metaheuristic Algorithms

Chapter 2: Literature Survey on Nature Inspired Optimisation Methodologies and Constraint Handling

Chapter 3: Cohort Intelligence (CI) Using the Static Penalty Function (SPF) Approach

Chapter 4: Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach

Chapter 5: Hybridization of Cohort Intelligence with Colliding Bodies Optimisation

Chapter 6: Validation of CI-SPF, CI-SAPF and CI-SAPF-CBO for Solving Discrete/Integer and Mixed Variable Problems

Chapter 7: Solution to Real-World Applications

Chapter 8: Conclusions and Recommendations

Appendix: Problem Statements for the Truss Structure, Design Engineering, Linear and Nonlinear Programming and Manufacturing Problems

Index

Erscheinungsdatum
Reihe/Serie Advances in Metaheuristics
Zusatzinfo 64 Tables, black and white; 75 Line drawings, black and white; 75 Illustrations, black and white
Verlagsort London
Sprache englisch
Maße 156 x 234 mm
Gewicht 381 g
Themenwelt Informatik Software Entwicklung Spieleprogrammierung
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Wirtschaft Betriebswirtschaft / Management
ISBN-10 1-032-15657-0 / 1032156570
ISBN-13 978-1-032-15657-6 / 9781032156576
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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