一種基於混沌的非線性最優化問題-作業調度問題 (英文) 9787576706789 M.A.艾爾-薩爾巴吉 S.納斯爾

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書名:一種基於混沌的非線性最優化問題-作業調度問題 (英文)
ISBN:9787576706789
出版社:哈爾濱工業大學
著編譯者:M.A.艾爾-薩爾巴吉 S.納斯爾
頁數:182
所在地:中國大陸 *此為代購商品
書號:1575919
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【台灣高等教育出版社簡體書】 一種基於混沌的非線性最優化問題-作業調度問題 (英文) 787576706789 M.A.艾爾-薩爾巴吉 S.納斯爾

內容簡介

本書展示了一種新的混合優化方法來解決最重要的最優化問題之一——非線性最優化問題。本書共包含六章內容,第一章提出了最優化問題的數學模型;第二章致力於介紹遺傳演算法的工作原理,並解釋了遺傳演算法是如何應用到解最優化問題之中的;第三章提出了解非線性最優化問題的一個新演算法;第四章提出了作業安排調度問題的結構,引入了作業安排調度問題的公式化;第五章的目的是實施解作業安排調度問題的新方法,並解釋了它的細節;第六章為結論以及給未來研究者的幾點建議。

目錄

List of Figures
List of Tables
Abstract
CHAPTER 1: A Survey on Related Topoes
1 1 Introduction
1 2 Mathematical Model of Optimization Problems
1 3 Classification of optimization problems
1 3 1 Classification based on existence of constraints
1 3 2 Classification based on nature of the design variables
1 3 3 Classification based on physical structure of the problem
1 3 4 Classification based on nature of the equations involved
1 3 5 Classification based on permissible values of the design variables
1 3 6 Classification based on deterministic nature of the variables
1 3 7 Classification based on separability of the functions
1 3 8 Classification based on number of the objective functions
1 4 Optimization Techniques
1 4 1 Classical Optimization Techniques
1 4 1 1 Nonlinear Programming
1 4 2 Advanced Techniques
1 4 2 1 Genetic algorithm (GA)
1 4 2 2 Simulated annealing (SA)
1 4 2 3 Neural network optimization
1 4 2 4 Tabu search (TS)
1 4 2 5 Ant colony optimization (ACO)
1 4 2 6 Particle swarm optimization (PSO)
1 4 2 7 Harmony search (HS)
1 4 2 8 Artificial bee colony (ABC)
CHAPTER 2: Genetic Algorithm
2 1 Introduction
2 2 Working Principle of GA
2 3 Genetic algorithm procedure for optimization problems
2 3 1 Encoding
2 3 2 Initial Population
2 3 3 Evaluation
2 3 4 Create new population
2 3 4 1 Selection
2 3 4 2 Crossover
2 3 4 3 Mutation
2 3 5 Repair
2 3 6 Migration
2 3 7 Termination Test
2 4 Genetic algorithm Parameters
2 4 1 Crossover probability
2 4 2 Mutation probability(Pro)
2 4 3 Population Size
2 5 Advantages and disadvantages of GA
2 5 1 Advantages of GA
2 5 2 Disadvantages of GA
CHAPTER 3: A Chaos-based Evolutionary Algorithm for General Nonlinear Programming Problems
3 1 Introduction
3 2 Chaos Theory
3 3 Chaotic maps
3 4 The proposed algorithm
3 4 1 Phase I: GA
3 4 2 Phase II : Chaotic local search
3 5 Experimental results
3 5 1 Test function
3 5 1 1 Unconstrained benchmark problems
3 5 1 2 Constrained benchmark problems
3 5 2 Performance Analysis Using Different Chaotic Maps
3 5 3 Performance Analysis using logistic map
3 5 4 Speed Convergence analysis
3 6 Conclusion
CHAPTER 4: Job Shop Scheduling Problems
4 1 Introduction
4 2 Scheduling Problem Types
4 3 Job shop scheduling problem structure
4 4 Job shop scheduling problem formulation
4 4 1 Mathematical representation of JSSP
4 4 2 Disjunctive graph
4 4 3 Gantt-Chart
4 5 Complexity of JSSP
4 6 Job shop scheduling solving techniques
4 6 1 Exact techniques
4 6 1 1 Mathematical techniques
4 6 1 2 Enumerative techniques
4 6 1 3 Decomposition strategies
4 6 2 Approximate techniques
4 6 2 1 Constructive Methods
4 6 2 2 Insertion Algorithms
4 6 2 3 Evolutionary Methods
4 6 2 4 Local Search Techniques
CHAPTER 5: Hybrid Genetic Algorithm for Job Shop Scheduling Problems
5 1 Introduction
5 2 The proposed algorithm (HGA)
5 2 1 Phase I: GA
5 2 2 Phase II: Local search
5 3 Experimental Results
5 3 1 Test Problems
5 3 2 Results and discnssion
5 4 Conclusion
CHAPTER 6: Conclusions and Future Work
6 1 Conclusions
6 2 Future Work
Bibliography
編輯手記

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