Simulated annealing: Difference between revisions
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Stewards: Nathan Preuss, Wei-Han Chen, Tianqi Xiao, Guoqing Hu | Stewards: Nathan Preuss, Wei-Han Chen, Tianqi Xiao, Guoqing Hu | ||
== Introduction == | |||
Simulated annealing (SA) is a probabilistic optimization algorithm inspired by the metallurgical annealing process, which reduces defects in a material by controlling the cooling rate to achieve a stable state.<ref>Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E.(1953). "Equation of state calculations by fast computing machines." ''Journal of Chemical Physics'', 21(6), 1087-1092.</ref> The core concept of SA is to allow algorithms to escape the constraints of local optima by occasionally accepting suboptimal solutions. This characteristic enables SA to find near-global optima in large and complex search spaces.<ref>Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). "Optimization by simulated annealing." ''Science'', 220(4598), 671-680.</ref> During the convergence process, the probability of accepting a suboptimal solution diminishes over time. | |||
SA is widely applied in diverse fields such as scheduling,<ref>Aarts, E. H., & Korst, J. H. (1988). ''Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing''. Wiley.</ref> machine learning, and engineering design, and is particularly effective for combinatorial optimization problems that are challenging for deterministic methods.<ref>Cerny, V. (1985). "Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm." ''Journal of Optimization Theory and Applications'', 45(1), 41-51.</ref> First proposed in the 1980s by Kirkpatrick, Gelatt, and Vecchi, SA demonstrated its efficacy in solving various complex optimization problems through its analogy to thermodynamics.<ref>Kirkpatrick, S. (1984). "Optimization by simulated annealing: Quantitative studies." ''Journal of Statistical Physics'', 34(5-6), 975-986.</ref> Today, it remains a powerful heuristic often combined with other optimization techniques to enhance performance in challenging problem spaces. | |||
== Algorithm Discussion == | |||
=== Formal Description of the Algorithm === | |||
SA is a probabilistic technique for finding approximate solutions to optimization problems, particularly those with large search spaces. Inspired by the annealing process in metallurgy, the algorithm explores the solution space by occasionally accepting worse solutions with a probability that diminishes over time. This reduces the risk of getting stuck in local optima and increases the likelihood of discovering the global optimum. | |||
The basic steps of the SA algorithm are as follows: | |||
# Initialization: Begin with an initial solution $$s_0$$ and an initial temperature $$T_0$$. | |||
# Iterative Improvement: | |||
# Termination: | |||
Revision as of 18:02, 12 December 2024
Author: Gwen Zhang (xz929), Yingjie Wang (yw2749), Junchi Xiao (jx422), Yichen Li (yl3938), Xiaoxiao Ge (xg353) (ChemE 6800 Fall 2024)
Stewards: Nathan Preuss, Wei-Han Chen, Tianqi Xiao, Guoqing Hu
Introduction
Simulated annealing (SA) is a probabilistic optimization algorithm inspired by the metallurgical annealing process, which reduces defects in a material by controlling the cooling rate to achieve a stable state.[1] The core concept of SA is to allow algorithms to escape the constraints of local optima by occasionally accepting suboptimal solutions. This characteristic enables SA to find near-global optima in large and complex search spaces.[2] During the convergence process, the probability of accepting a suboptimal solution diminishes over time.
SA is widely applied in diverse fields such as scheduling,[3] machine learning, and engineering design, and is particularly effective for combinatorial optimization problems that are challenging for deterministic methods.[4] First proposed in the 1980s by Kirkpatrick, Gelatt, and Vecchi, SA demonstrated its efficacy in solving various complex optimization problems through its analogy to thermodynamics.[5] Today, it remains a powerful heuristic often combined with other optimization techniques to enhance performance in challenging problem spaces.
Algorithm Discussion
Formal Description of the Algorithm
SA is a probabilistic technique for finding approximate solutions to optimization problems, particularly those with large search spaces. Inspired by the annealing process in metallurgy, the algorithm explores the solution space by occasionally accepting worse solutions with a probability that diminishes over time. This reduces the risk of getting stuck in local optima and increases the likelihood of discovering the global optimum.
The basic steps of the SA algorithm are as follows:
- Initialization: Begin with an initial solution $$s_0$$ and an initial temperature $$T_0$$.
- Iterative Improvement:
- Termination:
- ↑ Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E.(1953). "Equation of state calculations by fast computing machines." Journal of Chemical Physics, 21(6), 1087-1092.
- ↑ Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). "Optimization by simulated annealing." Science, 220(4598), 671-680.
- ↑ Aarts, E. H., & Korst, J. H. (1988). Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. Wiley.
- ↑ Cerny, V. (1985). "Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm." Journal of Optimization Theory and Applications, 45(1), 41-51.
- ↑ Kirkpatrick, S. (1984). "Optimization by simulated annealing: Quantitative studies." Journal of Statistical Physics, 34(5-6), 975-986.