Beschreibung
InhaltsangabeTheory Overview of Stochastic Optimization Algorithms.- General Remarks.- Exact Optimization Algorithms for Simple Problems.- Exact Optimization Algorithms for Complex Problems.- Monte Carlo.- Overview of Optimization Heuristics.- Implementation of Constraints.- Parallelization Strategies.- Construction Heuristics.- Markovian Improvement Heuristics.- Local Search.- Ruin & Recreate.- Simulated Annealing.- Threshold Accepting and Other Algorithms Related to Simulated Annealing.- Changing the Energy Landscape.- Estimation of Expectation Values.- Cooling Techniques.- Estimation of Calculation Time Needed.- Weakening the Pure Markovian Approach.- Neural Networks.- Genetic Algorithms and Evolution Strategies.- Optimization Algorithms Inspired by Social Animals.- Optimization Algorithms Based on Multiagent Systems.- Tabu Search.- Histogram Algorithms.- Searching for Backbones.- Applications.- General Remarks.- The Traveling Salesman Problem.- The Traveling Salesman Problem.- Extensions of Traveling Salesman Problem.- Application of Construction Heuristics to TSP.- Local Search Concepts Applied to TSP.- Next Larger Moves Applied to TSP.- Ruin & Recreate Applied to TSP.- Application of Simulated Annealing to TSP.- Dependencies of SA Results on Moves and Cooling Process.- Application to TSP of Algorithms Related to Simulated Annealing.- Application of Search Space Smoothing to TSP.- Further Techniques Changing the Energy Landscape of a TSP.- Application of Neural Networks to TSP.- Application of Genetic Algorithms to TSP.- Social Animal Algorithms Applied to TSP.- Simulated Trading Applied to TSP.- Tabu Search Applied to TSP.- Application of History Algorithms to TSP.- Application of Searching for Backbones to TSP.- Simulating Various Types of Government with Searching for Backbones.- The Constraint Satisfaction Problem.- The Constraint Satisfaction Problem.- Construction Heuristics for CSP.- Random Local Iterative Search Heuristics.- Belief Propagation and Survey Propagation.- Outlook.- Future Outlook of Optimization Business.
Inhaltsverzeichnis
General Remarks.- Exact Optimization Algorithms for Simple Problems.- Exact Optimization Algorithms for Complex Problems.- Monte Carlo.- Overview of Optimization Heuristics.- Implementation of Constraints.-Parallelization Strategies.- Construction Heuristics.- Markovian Improvement Heuristics.- Local Search.- Ruin & Recreate.- Simulated Annealing.- Threshold Accepting and Other Algorithms Related to Simulated Annealing.- Changing The Energy Landscape.- Estimation of Expectation Values.- Cooling Techniques.- Estimation of the Calculation Time Needed.- Weakening the Pure Markovian Approach.-Neural Networks.- Genetic Algorithms and Evolution Strategies.- Optimization Algorithms Inspired by Social Animals.- Optimization Algorithms Based on Multi Agent Systems.- Tabu Search.- Histogram Algorithms.- Searching for Backbones.- The Travelling Salesman Problem.- Extensions of the Traveling Salesman Problem.- Application of Construction Heuristics of theTSP.- Local Search Concepts Applied to the TSP.- Next Larger Moves Applied to the TSP.- Ruin and Recreate Applied to the TSP.- Application of Simulated Annealing to the TSP.-Dependencies of the SA-Results on the Moves and the Cooling Process.- Applicaton of Algorithms. Related to Simulated Annealing to the TSP.-Application of Search Space Smoothing to the TSP.-Further Techniques Changing the Energy Landscape of a TSP.- Applicaton of Neural Networks to the TSP. Application of Genetic Algorithms to the TSP.- Social Animal Algorithms Applied to the TSP.- Simulated Trading Applied to the TSP.- Tabu Search Applied to the TSP.- Application of History Algorithms to the TSP.- Application of Searching for Backbones to the TSP.- Simulating Various Types of Government With Searching for Backbones.- The Constraint Satisfaction Problem.- Construction Heuristics for the CSP.- Random Local Iterative Search Heuristics.- Belief Propagation and Survey Propagation.- Outlook for the Future of the Optimization Business.