Foundations of Computational Intelligence Volume 3 [electronic resource] : Global Optimization /
Abraham, Ajith.editor.
Hassanien, Aboul-Ella.editor.
Siarry, Patrick.editor.
Engelbrecht, Andries.editor.
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.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Global Optimization Algorithms: Theoretical Foundations and Perspectives -- Genetic Algorithms for the Use in Combinatorial Problems -- Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications -- Global Optimization Using Harmony Search: Theoretical Foundations and Applications -- Hybrid GRASP Heuristics -- Particle Swarm Optimization: Performance Tuning and Empirical Analysis -- Tabu Search to Solve Real-Life Combinatorial Optimization Problems: A Case of Study -- Reformulations in Mathematical Programming: A Computational Approach -- Graph-Based Local Elimination Algorithms in Discrete Optimization -- Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems -- Turbulent Particle Swarm Optimization Using Fuzzy Parameter Tuning -- Global Optimization Algorithms: Applications -- An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy -- Evolutionary Computing in Statistical Data Analysis -- Meta-heuristics for System Design Engineering -- Transgenetic Algorithm: A New Endosymbiotic Approach for Evolutionary Algorithms -- Multi-objective Team Forming Optimization for Integrated Product Development Projects -- Genetic Algorithms for Task Scheduling Problem -- PSO_Bounds: A New Hybridization Technique of PSO and EDAs.
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.
Artificial intelligence.
Applied mathematics.
Engineering mathematics.
Artificial Intelligence.
Mathematical and Computational Engineering.

Studies in Computational Intelligence