Evolutionary Constrained Optimization
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Beschreibung
This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.
This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.
Self-contained collection of current research addressing the general constrained optimization problems Contains specific chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization Includes well-illustrated case studies that would help industrial researchers to comprehend ways to apply the constrained optimization methodologies in practice Exhibits modern and computationally efficient constraint handling methods that every student and researchers new to optimization should know Useful material for researchers, novice, and experts alike, and also for classroom teaching as well as future research Includes supplementary material: sn.pub/extras
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Rituparna Datta
Themen in »Evolutionary Constrained Optimization«
Computational Intelligence Constrained Optimization Constraint Handling Evolutionary Computation Hybrid Optimization Multi-objective Optimization Penalty Function Soft Computing
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“The book contains ten chapters, each one dealing with various aspects of the current state of the art in the field of evolutionary optimization algorithms applied to general constrained optimization problems. … It will be useful for practitioners dealing with hard constrained optimization problems, as well as researchers and graduate students in the computer science and engineering fields.” (Petrica Pop, Computing Reviews, October, 2015)
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