Best-So-Far vs. Where-You-Are: Implications for Optimal Finite-Time Annealing (1994)
| Venue: | Systems and Control Letters |
| Citations: | 8 - 1 self |
BibTeX
@ARTICLE{Boese94best-so-farvs.,
author = {Kenneth D. Boese and Andrew B. Kahng},
title = {Best-So-Far vs. Where-You-Are: Implications for Optimal Finite-Time Annealing},
journal = {Systems and Control Letters},
year = {1994},
volume = {22},
pages = {71--78}
}
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Abstract
The simulated annealing (SA) algorithm is widely used for heuristic global optimization due to its high-quality results and its ability, in theory, to yield optimal solutions with probability one. Standard SA implementations use monotone decreasing, or "cooling", temperature schedules that are motivated by the algorithm's proof of optimality as well as by an analogy with statistical thermodynamics. In this paper, we challenge this motivation. The theoretical framework under which monotone cooling schedules are "optimal" fails to capture the practical performance of the algorithm; we therefore propose a "best-so-far" (BSF) criterion that measures the practical utility of a given annealing schedule. For small instances of two classic combinatorial problems, we determine annealing schedules that are optimal in terms of expected cost of the output solution. When the goal is to optimize the cost of the last solution seen by the algorithm (the "where-you-are" criterion used in previous theor...
