Ebook: Foundations of Computational Intelligence Volume 3: Global Optimization

Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.

This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

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Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.

This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

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Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.

This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Genetic Algorithms for the Use in Combinatorial Problems....Pages 3-22
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications....Pages 23-55
Global Optimization Using Harmony Search: Theoretical Foundations and Applications....Pages 57-73
Hybrid GRASP Heuristics....Pages 75-100
Particle Swarm Optimization: Performance Tuning and Empirical Analysis....Pages 101-128
Tabu Search to Solve Real-Life Combinatorial Optimization Problems: A Case of Study....Pages 129-151
Reformulations in Mathematical Programming: A Computational Approach....Pages 153-234
Graph-Based Local Elimination Algorithms in Discrete Optimization....Pages 235-266
Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems....Pages 267-289
Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning....Pages 291-312
Front Matter....Pages 313-313
An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy....Pages 315-346
Evolutionary Computing in Statistical Data Analysis....Pages 347-386
Meta-heuristics for System Design Engineering....Pages 387-423
Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms....Pages 425-460
Multi-objective Team Forming Optimization for Integrated Product Development Projects....Pages 461-478
Genetic Algorithms for Task Scheduling Problem....Pages 479-507
PSO_Bounds: A New Hybridization Technique of PSO and EDAs....Pages 509-526
Back Matter....Pages -

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Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc.

Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems.

This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Genetic Algorithms for the Use in Combinatorial Problems....Pages 3-22
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications....Pages 23-55
Global Optimization Using Harmony Search: Theoretical Foundations and Applications....Pages 57-73
Hybrid GRASP Heuristics....Pages 75-100
Particle Swarm Optimization: Performance Tuning and Empirical Analysis....Pages 101-128
Tabu Search to Solve Real-Life Combinatorial Optimization Problems: A Case of Study....Pages 129-151
Reformulations in Mathematical Programming: A Computational Approach....Pages 153-234
Graph-Based Local Elimination Algorithms in Discrete Optimization....Pages 235-266
Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems....Pages 267-289
Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning....Pages 291-312
Front Matter....Pages 313-313
An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy....Pages 315-346
Evolutionary Computing in Statistical Data Analysis....Pages 347-386
Meta-heuristics for System Design Engineering....Pages 387-423
Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms....Pages 425-460
Multi-objective Team Forming Optimization for Integrated Product Development Projects....Pages 461-478
Genetic Algorithms for Task Scheduling Problem....Pages 479-507
PSO_Bounds: A New Hybridization Technique of PSO and EDAs....Pages 509-526
Back Matter....Pages -
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