Издательство InTech, 2008, -428 pp.
Optimization is important in all branches of engineering due to limited resources available. Through optimization, maximum usage of the resource can be achieved. However, global optimization can be difficult due to the requirement of the knowledge of the system behavior under analysis and the possible large solution space. Without this knowledge, the optimization thus obtained may only be a local optimization. Metaheuristic algorithms, on the other hand, are effective in exploring the solution space. Often, they are referred to as black box algorithms as they use very limited knowledge about the specific system to be tackled, and often it does not require a mathematical model of the system under study. Hence it can be used to solve a broad range of problem, and has thus receiving increasing attention.
One of the commonly used metaheuristic algorithms is the Simulated Annealing (SA). SA is an optimization algorithm that is not fool by false minima and is easy to implement. It is also superior as compared to many other metaheuristic algorithms as presented in this book. In this book, the different applications of the Simulated Annealing will be presented. The first 11 chapters are devoted to the applications in Industrial engineering such as the scheduling problem, decision making, allocation problem, routing problem and general optimization problem.
The subsequent chapters of this book will focus on the application of the Simulated Annealing in Material Engineering on porous material study, Electrical Engineering on integrated circuit technology, Mechanical Engineering on mechanical structure design, Structural Engineering on concrete structures, Computer Engineering on task mapping and Bio-engineering on protein structure. The last three Chapters will be on the methodology to optimize the Simulated Annealing, its comparison with other metaheuristic algorithms and the various practical considerations in the application of Simulated Annealing.
This book provides the readers with the knowledge of Simulated Annealing and its vast applications in the various branches of engineering. We encourage readers to explore the application of Simulated Annealing in their work for the task of optimization.
Simulated Annealing as an Intensification Component in Hybrid Population-Based Metaheuristics.
Multi-objective Simulated Annealing for a Maintenance Workforce Scheduling Problem: A case Study.
Using Simulated Annealing for Open Shop Scheduling with Sum Criteria.
Real Time Multiagent Decision Making by Simulated Annealing.
Leaing FCM with Simulated Annealing.
Knowledge-Informed Simulated Annealing for Spatial Allocation Problems.
An Efficient Quasi-Human Heuristic Algorithm for Solving the Rectangle-Packing Problem.
Application of Simulated Annealing to Routing Problems in City Logistics.
Theory and Applications of Simulated Annealing for Nonlinear Constrained Optimization.
Annealing Stochastic Approximation Monte Carlo for Global Optimization.
Application of Simulated Annealing on the Study of Multiphase Systems.
Simulated Annealing for Mixture Distribution Analysis and its Applications to Reliability Testing.
Reticle Floorplanning and Simulated Wafer Dicing for Multiple-project Wafers by Simulated Annealing.
Structural Optimization Using Simulated Annealing.
Optimization of Reinforced Concrete Structures by Simulated Annealing.
Best Practices for Simulated Annealing in Multiprocessor Task Distribution Problems.
Simulated Annealing of Two Electron Density Solution Systems.
Improving the Neighborhood Selection Strategy in Simulated Annealing using the Optimal Stopping Problem.
A Comparison of Simulated Annealing, Elliptic and Genetic Algorithms for Finding Irregularly Shaped Spatial Clusters.
Practical Considerations for Simulated Annealing Implementation.
Optimization is important in all branches of engineering due to limited resources available. Through optimization, maximum usage of the resource can be achieved. However, global optimization can be difficult due to the requirement of the knowledge of the system behavior under analysis and the possible large solution space. Without this knowledge, the optimization thus obtained may only be a local optimization. Metaheuristic algorithms, on the other hand, are effective in exploring the solution space. Often, they are referred to as black box algorithms as they use very limited knowledge about the specific system to be tackled, and often it does not require a mathematical model of the system under study. Hence it can be used to solve a broad range of problem, and has thus receiving increasing attention.
One of the commonly used metaheuristic algorithms is the Simulated Annealing (SA). SA is an optimization algorithm that is not fool by false minima and is easy to implement. It is also superior as compared to many other metaheuristic algorithms as presented in this book. In this book, the different applications of the Simulated Annealing will be presented. The first 11 chapters are devoted to the applications in Industrial engineering such as the scheduling problem, decision making, allocation problem, routing problem and general optimization problem.
The subsequent chapters of this book will focus on the application of the Simulated Annealing in Material Engineering on porous material study, Electrical Engineering on integrated circuit technology, Mechanical Engineering on mechanical structure design, Structural Engineering on concrete structures, Computer Engineering on task mapping and Bio-engineering on protein structure. The last three Chapters will be on the methodology to optimize the Simulated Annealing, its comparison with other metaheuristic algorithms and the various practical considerations in the application of Simulated Annealing.
This book provides the readers with the knowledge of Simulated Annealing and its vast applications in the various branches of engineering. We encourage readers to explore the application of Simulated Annealing in their work for the task of optimization.
Simulated Annealing as an Intensification Component in Hybrid Population-Based Metaheuristics.
Multi-objective Simulated Annealing for a Maintenance Workforce Scheduling Problem: A case Study.
Using Simulated Annealing for Open Shop Scheduling with Sum Criteria.
Real Time Multiagent Decision Making by Simulated Annealing.
Leaing FCM with Simulated Annealing.
Knowledge-Informed Simulated Annealing for Spatial Allocation Problems.
An Efficient Quasi-Human Heuristic Algorithm for Solving the Rectangle-Packing Problem.
Application of Simulated Annealing to Routing Problems in City Logistics.
Theory and Applications of Simulated Annealing for Nonlinear Constrained Optimization.
Annealing Stochastic Approximation Monte Carlo for Global Optimization.
Application of Simulated Annealing on the Study of Multiphase Systems.
Simulated Annealing for Mixture Distribution Analysis and its Applications to Reliability Testing.
Reticle Floorplanning and Simulated Wafer Dicing for Multiple-project Wafers by Simulated Annealing.
Structural Optimization Using Simulated Annealing.
Optimization of Reinforced Concrete Structures by Simulated Annealing.
Best Practices for Simulated Annealing in Multiprocessor Task Distribution Problems.
Simulated Annealing of Two Electron Density Solution Systems.
Improving the Neighborhood Selection Strategy in Simulated Annealing using the Optimal Stopping Problem.
A Comparison of Simulated Annealing, Elliptic and Genetic Algorithms for Finding Irregularly Shaped Spatial Clusters.
Practical Considerations for Simulated Annealing Implementation.