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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
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