
Accession Number : ADA185547
Title : Optimization by Simulated Annealing: A TimeComplexity Analysis.
Descriptive Note : Doctoral thesis,
Corporate Author : ILLINOIS UNIV AT URBANA DEPT OF ELECTRICAL ENGINEERING
Personal Author(s) : Sasaki, Galen H
PDF Url : ADA185547
Report Date : Oct 1987
Pagination or Media Count : 113
Abstract : In this thesis, results of a study of the heuristic random search optimization method called simulated annealing are given. Most of the results are concerned with the average amount of time simulated annealing takes to find an acceptable solution. We analyzed the average time complexity of simulated annealing for the matching problem. Although the matching problem has worstcase polynomial time complexity, we show that there is a sequence of graphs where the average time complexity of a natural version of simulated annealing has a worstcase polynomial average time complexity if it is only required to find near maximum matchings. An exponential lower bound on the minimum average time complexity over a wide class of simulated annealing algorithms when our attention is restricted to constant temperature schedules is also given. The typical case for simulated annealing for the matching problem is also analyzed. Since we were not able to discover a method to exactly analyze the average time complexity of simulated annealing for the matching problem for typical graphs, we used approximations to estimate the average time complexity and then checked the accuracy of the approximation with data from computer simulations. Our results indicate that if we only consider graphs that have at least as many edges as they have nodes then the average time complexity of simulated annealing for a typical graph with n nodes is o (n4). A technique for producing easytoanalyze annealing processes, called the template method, is given.
Descriptors : *ANNEALING, *COMPUTERIZED SIMULATION, *OPTIMIZATION, ALGORITHMS, APPROXIMATION(MATHEMATICS), GRAPHS, NODES, POLYNOMIALS, SEQUENCES, SIMULATION, TEMPLATES, MATHEMATICAL ANALYSIS, HEURISTIC METHODS, ERROR CORRECTION CODES, STATISTICAL DECISION THEORY
Subject Categories : Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE