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Synopsis
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.
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About the Author
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.
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Constraint Handling in Cohort Intelligence Algorithm
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Constraint Handling in Cohort Intelligence Algorithm (Paperback)
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781032156576
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Constraint Handling in Cohort Intelligence Algorithm
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