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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| Định dạng: | Điện tử eBook |
| Ngôn ngữ: | Tiếng Anh |
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[S.l.] :
ELSEVIER,
2024.
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| Truy cập trực tuyến: | ScienceDirect |
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| 100 | 1 | |a Xu, Hua |c (Professor in computer science) | |
| 245 | 1 | 0 | |a INTELLIGENT EVOLUTIONARY OPTIMIZATION. |
| 260 | |a [S.l.] : |b ELSEVIER, |c 2024. | ||
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| 520 | |a 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-dimensional spaces, particularly for high-dimensional multi-objective optimization problems, offering practical guidance and effective solutions across various domains. Providing practical solutions, methods, and tools to tackle complex optimization problems and enhance modern optimization techniques, this book will be a valuable resource for professionals seeking to enhance their understanding and proficiency in intelligent evolutionary optimization. | ||
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 650 | 0 | |a Evolutionary computation. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Genetic algorithms. | |
| 650 | 6 | |a Réseaux neuronaux à structure évolutive. |0 (CaQQLa)201-0299688 | |
| 650 | 6 | |a Optimisation mathématique. |0 (CaQQLa)201-0007680 | |
| 650 | 6 | |a Algorithmes génétiques. |0 (CaQQLa)201-0265158 | |
| 650 | 7 | |a Evolutionary computation |2 fast |0 (OCoLC)fst00917338 | |
| 650 | 7 | |a Genetic algorithms |2 fast |0 (OCoLC)fst00939996 | |
| 650 | 7 | |a Mathematical optimization |2 fast |0 (OCoLC)fst01012099 | |
| 700 | 1 | |a Yuan, Yuan. | |
| 776 | 0 | 8 | |i Print version: |z 0443274002 |z 9780443274008 |w (OCoLC)1414458582 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443274008 |