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Über dieses Buch

Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorithm; instead it is a general advanced optimization mechanism which is highly scalable with robustness and randomness. Therefore, this book demonstrates the flexibility of these algorithms, as well as their robustness and reusability in order to solve mass complicated problems in manufacturing.

Since the genetic algorithm was presented decades ago, a large number of intelligent optimization algorithms and their improvements have been developed. However, little work has been done to extend their applications and verify their competence in solving complicated problems in manufacturing.

This book will provide an invaluable resource to students, researchers, consultants and industry professionals interested in engineering optimization. It will also be particularly useful to three groups of readers: algorithm beginners, optimization engineers and senior algorithm designers. It offers a detailed description of intelligent optimization algorithms to algorithm beginners; recommends new configurable design methods for optimization engineers, and provides future trends and challenges of the new configuration mechanism to senior algorithm designers.

Inhaltsverzeichnis

Frontmatter

Introduction and Overview

Frontmatter

Chapter 1. Brief History and Overview of Intelligent Optimization Algorithms

Abstract
Up to now, intelligent optimization algorithm has been developed for nearly 40 years. It is one of the main research directions in the field of algorithm and artificial intelligence. No matter for complex continuous problems or discrete NP-hard combinatorial optimizations, people nowadays is more likely to find a feasible solution by using such randomized iterative algorithm within a short period of time instead of traditional deterministic algorithms. In this chapter, the basic principle of algorithms, research classifications, and the development trends of intelligent optimization algorithm are elaborated.
Fei Tao, Yuanjun Laili, Lin Zhang

Chapter 2. Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Abstract
Due to its good versatility and independence, intelligent optimization algorithm has largely shortened the time of decision-making in large-scale optimization problems of manufacture. However, lower searching time often conflicts with the searching accuracy in most cases.
Fei Tao, Yuanjun Laili, Lin Zhang

Design and Implementation

Frontmatter

Chapter 3. Dynamic Configuration of Intelligent Optimization Algorithms

Abstract
Since genetic algorithm (GA) presented decades ago, large amount of intelligent optimization algorithms and their improvements and mixtures have been putting forward one after another.
Fei Tao, Yuanjun Laili, Lin Zhang

Chapter 4. Improvement and Hybridization of Intelligent Optimization Algorithm

Abstract
Algorithm improvement and hybridization are two important branches in the development of intelligent optimization algorithm.
Fei Tao, Lin Zhang, Yuanjun Laili

Chapter 5. Parallelization of Intelligent Optimization Algorithm

Abstract
Today, different kinds of hardware for computing are more and more powerful, in accordance with large scaled complex computing tasks. From multi-core computer to clusters, various parallel architectures are developed for computing acceleration. In terms of the long time iteration and population based mechanism of intelligent optimization algorithm, parallelization is attainable and imperative in many complex optimization.
Fei Tao, Lin Zhang, Yuanjun Laili

Application of Improved Intelligent Optimization Algorithms

Frontmatter

Chapter 6. GA-BHTR for Partner Selection Problem

Abstract
In this chapter, GA-BHTR (genetic algorithm maintained by using binary heap and transitive reduction) [1] for addressing partner selection problem (PSP) in a virtual enterprise [2] is introduced. Based on ordinary initialization, an improved binary heap strategy is configured before it with uniform population input and output to realize initialization improvement. It is designed to simplify the directed acrylic graph that represents the precedence relationship among the subprojects in PSP and enhance the searching diversity of the algorithm. Then, in order to avoid solutions from converging to a constant value early during evolution, multiple communities are used instead of a single community in GA-BHTR. Operators are configured in different communities independently. Communication among communities is executed by periodic interchange.
Fei Tao, Lin Zhang, Yuanjun Laili

Chapter 7. CLPS-GA for Energy-Aware Cloud Service Scheduling

Abstract
In this chapter, CLPS-GA (A Case Library and Pareto Solution-based improved Genetic Algorithm) [Appl Soft Comput 11(3):3056–3065, 2004] for addressing Energy-aware Cloud Service Scheduling (ECSS) in cloud manufacturing is introduced.
Fei Tao, Lin Zhang, Yuanjun Laili

Application of Hybrid Intelligent Optimization Algorithms

Frontmatter

Chapter 8. SFB-ACO for Submicron VLSI Routing Optimization with Timing Constraints

Abstract
The arrival of submicron era has created a huge difference on VLSI (very large scale integration): delay on interconnects has far exceeded that on gates so the total delay for a sink can no longer be simply assessed by the length of weighted edges which makes its routing more complicated than ever.
Fei Tao, Lin Zhang, Yuanjun Laili

Chapter 9. A Hybrid RCO for Dual Scheduling of Cloud Service and Computing Resource in Private Cloud

Abstract
In this chapter, the idea of combining SCOS and OACR into one-time decision in one console is presented, named Dual Scheduling of Cloud Services and Computing Resources (DS-CSCR) [1]. For addressing large-scale DS-CSCR problem, Ranking Chaos Optimization (RCO) is configured. With the consideration of large-scale irregular solution spaces, new adaptive chaos operator is designed to traverse wider spaces within a short time. Besides, dynamic heuristic and ranking selection are hybrid to control the chaos evolution in the proposed algorithm.
Yuanjun Laili, Fei Tao, Lin Zhang

Application of Parallel Intelligent Optimization Algorithms

Frontmatter

Chapter 10. Computing Resource Allocation with PEADGA

Abstract
In this chapter, for solving optimal allocation of computing resources (OACR) problem in cloud manufacturing (CMfg) [1], serial three-layer operation configuration and parallel configuration are both applied.
Yuanjun Laili, Fei Tao, Lin Zhang

Chapter 11. Job Shop Scheduling with FPGA-Based F4SA

Abstract
In this chapter, a new configured permutation-based feasible solution space searching simulated annealing algorithm (F4SA) is designed for solving job shop scheduling problem (JSSP). Firstly, a permutation-based encoding scheme is presented, which can make the solution always feasible in iteration. After that, simulated annealing operator, mutation operator and a new reverse order operator are implemented on FPGA and configured for updating solutions in parallel way. Each operator is encapsulated in a module and can be connected with fixed input, output and parameters. The searching time of intelligent optimization algorithm in FPGA is far shorter than which in general computer. The design and implementation of F4SA for JSSP presented in this chapter is just an example to demonstrate how to implement an intelligent optimization algorithm and dynamically configure multiple operators in FPGA. The searching accuracy of this algorithm is to be improved further.
Fei Tao, Lin Zhang, Yuanjun Laili

Future Works of Configurable Intelligent Optimization Algorithm

Frontmatter

Chapter 12. Future Trends and Challenges

Abstract
In this chapter, we give some future trends and challenges of dynamic configuration not only for intelligent optimization algorithm, but also for other algorithms used in the whole life cycle of manufacturing.
Fei Tao, Lin Zhang, Yuanjun Laili
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