Ebook: Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization

Meta-Heuristics: Advances and Trends in Local Search Paradigms forOptimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.

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Meta-Heuristics: Advances and Trends in Local Search Paradigms forOptimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.

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Meta-Heuristics: Advances and Trends in Local Search Paradigms forOptimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.
Content:
Front Matter....Pages i-xii
Front Matter....Pages 1-1
Tabu Search Algorithms and Lower Bounds for the Resource-Constrained Project Scheduling Problem....Pages 1-18
Metaheuristic for the Vehicle Routing Problem with Time Windows....Pages 19-36
New Heuristic Algorithms for the Crew Scheduling Problem....Pages 37-47
Enhanced Continuous Tabu Search: An Algorithm for Optimizing Multiminima Functions....Pages 49-61
Local Search in Constraint Programming: Experiments with Tabu Search on the Vehicle Routing Problem....Pages 63-76
Tabu Search for Graph Coloring, T-Colorings and Set T-Colorings....Pages 77-92
Tabu Search with Critical Event Memory: An Enhanced Application for Binary Quadratic Programs....Pages 93-109
Actuator Selection for the Control of Multi-Frequency Noise in Aircraft Interiors....Pages 111-124
Neighborhood Search Algorithm for the Guillotine Non-Oriented Two-Dimensional Bin Packing Problem....Pages 125-139
Candidate List and Exploration Strategies for Solving 0/1 Mip Problems Using a Pivot Neighborhood....Pages 141-154
Global and Local Moves in Tabu Search: A Real-Life Mail Collecting Application....Pages 155-174
Flow Line Scheduling by Tabu Search....Pages 175-189
Front Matter....Pages 191-191
Using Lower Bounds in Minimum Span Frequency Assignment....Pages 191-204
A Hybrid Heuristic for Multiobjective Knapsack Problems....Pages 205-212
Hybrid Genetic Tabu Search for a Cyclic Scheduling Problem....Pages 213-229
Front Matter....Pages 231-231
Adaptive Genetic Algorithms: A Methodology for Dynamic Autoconfiguration of Genetic Search Algorithms....Pages 231-248
The Lavish Ordering Genetic Algorithm....Pages 249-256
Fitness Landscapes and Performance of Meta-Heuristics....Pages 257-268
A Network-Based Adaptive Evolutionary Algorithm for Constraint Satisfaction Problems....Pages 269-283
Front Matter....Pages 285-285
Applying the ANT System to the Vehicle Routing Problem....Pages 285-296
Front Matter....Pages 285-285
Cooperative Intelligent Search Using Adaptive Memory Techniques....Pages 297-312
The Max-Min ANT System and Local Search for Combinatorial Optimization Problems....Pages 313-329
Front Matter....Pages 331-331
Towards an Evolutionary Method — Cooperating Multi-Thread Parallel Tabu Search Hybrid....Pages 331-344
Parallel Tabu Search for Large Optimization Problems....Pages 345-358
Sequential and Parallel Local Search Algorithms for Job Shop Scheduling....Pages 359-371
An Experimental Study of Systemic Behavior of Cooperative Search Algorithms....Pages 373-392
Front Matter....Pages 393-393
A Hopfield-Tank Neural Network Model for the Generalized Traveling Salesman Problem....Pages 393-402
Generalized Cybernetic Optimization: Solving Continuous Variable Problems....Pages 403-418
Solving the Progressive Party Problem by Local Search....Pages 419-432
An Introduction to Variable Neighborhood Search....Pages 433-458
A Variable Depth Search Algorithm for the Generalized Assignment Problem....Pages 459-471
Guided Local Search for the Vehicle Routing Problem with Time Windows....Pages 473-486
Memory Adaptive Reasoning & Greedy Assignment Techniques for the Capacitated Minimum Spanning Tree Problem....Pages 487-498
A Chunking Based Selection Strategy for Integrating Meta-Heuristics with Branch and Bound....Pages 499-511

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Meta-Heuristics: Advances and Trends in Local Search Paradigms forOptimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.
Content:
Front Matter....Pages i-xii
Front Matter....Pages 1-1
Tabu Search Algorithms and Lower Bounds for the Resource-Constrained Project Scheduling Problem....Pages 1-18
Metaheuristic for the Vehicle Routing Problem with Time Windows....Pages 19-36
New Heuristic Algorithms for the Crew Scheduling Problem....Pages 37-47
Enhanced Continuous Tabu Search: An Algorithm for Optimizing Multiminima Functions....Pages 49-61
Local Search in Constraint Programming: Experiments with Tabu Search on the Vehicle Routing Problem....Pages 63-76
Tabu Search for Graph Coloring, T-Colorings and Set T-Colorings....Pages 77-92
Tabu Search with Critical Event Memory: An Enhanced Application for Binary Quadratic Programs....Pages 93-109
Actuator Selection for the Control of Multi-Frequency Noise in Aircraft Interiors....Pages 111-124
Neighborhood Search Algorithm for the Guillotine Non-Oriented Two-Dimensional Bin Packing Problem....Pages 125-139
Candidate List and Exploration Strategies for Solving 0/1 Mip Problems Using a Pivot Neighborhood....Pages 141-154
Global and Local Moves in Tabu Search: A Real-Life Mail Collecting Application....Pages 155-174
Flow Line Scheduling by Tabu Search....Pages 175-189
Front Matter....Pages 191-191
Using Lower Bounds in Minimum Span Frequency Assignment....Pages 191-204
A Hybrid Heuristic for Multiobjective Knapsack Problems....Pages 205-212
Hybrid Genetic Tabu Search for a Cyclic Scheduling Problem....Pages 213-229
Front Matter....Pages 231-231
Adaptive Genetic Algorithms: A Methodology for Dynamic Autoconfiguration of Genetic Search Algorithms....Pages 231-248
The Lavish Ordering Genetic Algorithm....Pages 249-256
Fitness Landscapes and Performance of Meta-Heuristics....Pages 257-268
A Network-Based Adaptive Evolutionary Algorithm for Constraint Satisfaction Problems....Pages 269-283
Front Matter....Pages 285-285
Applying the ANT System to the Vehicle Routing Problem....Pages 285-296
Front Matter....Pages 285-285
Cooperative Intelligent Search Using Adaptive Memory Techniques....Pages 297-312
The Max-Min ANT System and Local Search for Combinatorial Optimization Problems....Pages 313-329
Front Matter....Pages 331-331
Towards an Evolutionary Method — Cooperating Multi-Thread Parallel Tabu Search Hybrid....Pages 331-344
Parallel Tabu Search for Large Optimization Problems....Pages 345-358
Sequential and Parallel Local Search Algorithms for Job Shop Scheduling....Pages 359-371
An Experimental Study of Systemic Behavior of Cooperative Search Algorithms....Pages 373-392
Front Matter....Pages 393-393
A Hopfield-Tank Neural Network Model for the Generalized Traveling Salesman Problem....Pages 393-402
Generalized Cybernetic Optimization: Solving Continuous Variable Problems....Pages 403-418
Solving the Progressive Party Problem by Local Search....Pages 419-432
An Introduction to Variable Neighborhood Search....Pages 433-458
A Variable Depth Search Algorithm for the Generalized Assignment Problem....Pages 459-471
Guided Local Search for the Vehicle Routing Problem with Time Windows....Pages 473-486
Memory Adaptive Reasoning & Greedy Assignment Techniques for the Capacitated Minimum Spanning Tree Problem....Pages 487-498
A Chunking Based Selection Strategy for Integrating Meta-Heuristics with Branch and Bound....Pages 499-511
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