INTELLIGENT EVOLUTIONARY OPTIMIZATION.
Intelligent Evolutionary Optimization introduces biologically-inspired intelligent optimization algorithms to address complex optimization problems and provide practical solutions for tackling combinatorial optimization problems. The book explores efficient search and optimization methods in high-di...
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| Format: | eBook |
| Language: | English |
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[S.l.] :
ELSEVIER,
2024.
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| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- 3.4.4 Computational complexity
- 3.4.5 Discussion
- 3.5 Experimental design
- 3.5.1 Test problems
- 3.5.2 Performance metrics
- 3.5.3 MOEAs for comparison
- 3.5.4 Experimental settings
- 3.6 Analysis of the performance of enhanced algorithms
- 3.6.1 Influence of parameter K
- 3.6.2 Investigation of convergence and diversity
- 3.7 Comparison with state-of-the-art algorithms
- 3.7.1 Comparison of normalized test problems
- 3.7.2 Comparison of scaled test problems
- 3.8 Conclusion
- References
- 4 Objective reduction in many-objective optimization: evolutionary multiobjective approaches and comprehensive analysis
- 4.1 Introduction
- 4.2 Preliminaries and background
- 4.2.1 Multi and many-objective optimization
- 4.2.2 Basic concepts in objective reduction
- 4.2.3 Pareto front-representation and misdirection
- 4.2.4 Existing approaches to objective reduction
- 4.2.4.1 Dominance relation preservation
- 4.2.4.2 Principal component analysis and maximum variance unfolding
- 4.3 Proposed multiobjective approaches
- 4.3.1 Dominance structure-based multiobjective formulation
- 4.3.2 Correlation-based multiobjective formulation
- 4.3.3 Using multiobjective evolutionary algorithms
- 4.3.4 Why multiobjective approaches
- 4.4 Analysis of dominance structure- and correlation-based approaches
- 4.4.1 Theoretical foundations
- 4.4.2 Strengths and limitations of dominance structure-based approaches
- 4.4.3 Strengths and limitations of correlation-based approaches
- 4.5 Benchmark experiments
- 4.5.1 Benchmark problems
- 4.5.2 Generation of sample sets
- 4.5.3 Algorithms in comparison
- 4.5.4 Investigation of the behavior of multiobjective approaches
- 4.5.5 Effectiveness of evolutionary multiobjective search
- 4.5.6 Comparison in identifying the essential objective set
- 4.6 Applications to real-world problems.
- 7.2.3.3 Active decoding of two-vector code
- 7.2.4 Initialize the harmony memory
- 7.2.4.1 Generating a two-vector code based on heuristics
- 7.2.4.2 Converting the two-vector code to the harmony vector
- 7.2.5 Improvise a new harmony
- 7.2.6 Problem-dependent local search
- 7.2.6.1 Disjunctive graph model
- 7.2.6.2 Neighborhoods based on common critical operations
- 7.2.6.3 Local search procedure
- 7.2.7 Update harmony memory
- 7.3 Experimental details
- 7.3.1 Experimental setup
- 7.3.2 Computational results and comparisons
- 7.3.3 Further comparisons of hybrid harmony search with other algorithms
- 7.4 Discussion
- 7.5 Conclusion
- References
- 8 Flexible job shop scheduling using hybrid differential evolution algorithms
- 8.1 Introduction
- 8.2 Basic differential evolution algorithm
- 8.2.1 Initialization
- 8.2.2 Mutation
- 8.2.3 Crossover
- 8.2.4 Selection
- 8.3 Proposed hybrid differential evolution for the flexible job shop scheduling problem
- 8.3.1 Overview of the hybrid differential evolution
- 8.3.2 Representation and initialization
- 8.3.3 Two-vector code
- 8.3.3.1 Machine assignment vector
- 8.3.3.2 Operation sequence vector
- 8.3.3.3 Encoding and decoding
- 8.3.4 Conversion techniques
- 8.3.4.1 Forward conversion
- 8.3.4.2 Backward conversion
- 8.3.5 Local search algorithm
- 8.3.5.1 Disjunctive graph
- 8.3.5.2 Neighborhood structures
- 8.3.5.3 Procedure of local search
- 8.4 Experimental studies
- 8.4.1 Experimental setup
- 8.4.2 Results of Kacem instances
- 8.4.3 Results of BRdata instances
- 8.4.4 Results of BCdata instances
- 8.4.5 Results of HUdata instances
- 8.4.6 Further performance analysis of hybrid differential evolution
- 8.4.6.1 Significance tests between hybrid differential evolution algorithms
- 8.4.6.2 Influence of parameters on hybrid differential evolution.
- 8.4.6.3 Effect of hybridizing differential evolution and local search algorithms
- 8.4.6.4 Performance potential of pure differential evolution algorithm
- 8.5 Conclusion
- References
- 9 An integrated search heuristic for large-scale flexible job shop scheduling problems
- 9.1 Introduction
- 9.2 Hybrid harmony search
- 9.2.1 Outline of harmony search
- 9.2.2 Procedure of hybrid harmony search
- 9.2.3 Adaptation of hybrid harmony search to the flexible job shop scheduling problem
- 9.2.3.1 Representation and initialization
- 9.2.3.2 Two-vector code
- 9.2.3.3 Conversion techniques
- 9.2.3.4 Local search strategy
- 9.3 Large neighborhood search
- 9.3.1 Outline of large neighborhood search
- 9.3.2 Constraint-based model for the flexible job shop scheduling problem
- 9.3.3 Destruction procedure
- 9.3.4 Construction procedure
- 9.4 Integrated search heuristic: hybrid harmony search/large neighborhood search
- 9.5 Experimental study
- 9.5.1 Experimental setup
- 9.5.2 Performance analysis of the hybrid harmony search module
- 9.5.3 Performance analysis of the large neighborhood search module
- 9.5.4 Effects of integration
- 9.5.5 Computational results on large-scale benchmark instances
- 9.6 Conclusion
- References
- 10 Multiobjective flexible job shop scheduling using memetic algorithms
- 10.1 Introduction
- 10.2 Background
- 10.2.1 Formulation of the multiobjective flexible job shop scheduling problem
- 10.2.2 Disjunctive graph model
- 10.2.3 Brief Introduction to nondominated sorting genetic algorithm II
- 10.2.4 Memetic algorithms for multiobjective combinatorial optimization
- 10.3 Overview of the proposed memetic algorithms
- 10.4 Exploration using genetic search
- 10.4.1 Chromosome encoding
- 10.4.2 Chromosome decoding
- 10.4.3 Genetic operators
- 10.5 Exploitation using local search.