Combining simulated annealing with local search heuristics

@article{Martin1996CombiningSA,
  title={Combining simulated annealing with local search heuristics},
  author={Olivier C. Martin and Steve W. Otto},
  journal={Annals of Operations Research},
  year={1996},
  volume={63},
  pages={57-75}
}
  • O. Martin, S. Otto
  • Published 1 September 1993
  • Computer Science
  • Annals of Operations Research
We introduce a meta-heuristic to combine simulated annealing with local search methods for CO problems. This new class of Markov chains leads to significantly more powerful optimization methods than either simulated annealing or local search. The main idea is to embed deterministic local search techniques into simulated annealing so that the chain explores only local optima. It makes large, global changes, even at low temperatures, thus overcoming large barriers in configuration space. We have… 
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References

SHOWING 1-10 OF 49 REFERENCES
Large-Step Markov Chains for the Traveling Salesman Problem
TLDR
A new class of Markov chain Monte Carlo search procedures are introduced, leading to more powerful optimizat~on methods than simulated annealing, and a new best heuristic is introduced.
Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning
TLDR
This paper discusses annealing and its parameterized generic implementation, describes how this generic algorithm was adapted to the graph partitioning problem, and reports how well it compared to standard algorithms like the Kernighan-Lin algorithm.
Computer solutions of the traveling salesman problem
TLDR
Two algorithms for solving the (symmetric distance) traveling salesman problem have been programmed for a high-speed digital computer and are based on a general heuristic approach believed to be of general applicability to various optimization problems.
Large-step markov chains for the TSP incorporating local search heuristics
Local Optimization and the Traveling Salesman Problem
TLDR
This paper surveys the state of the art with respect to the TSP, with emphasis on the performance of traditional local optimization algorithms and their new competitors, and on what insights complexity theory does, or does not, provide.
An Effective Heuristic Algorithm for the Traveling-Salesman Problem
This paper discusses a highly effective heuristic procedure for generating optimum and near-optimum solutions for the symmetric traveling-salesman problem. The procedure is based on a general
Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm
TLDR
It is conjecture that the analogy with thermodynamics can offer a new insight into optimization problems and can suggest efficient algorithms for solving them.
A Branch-and-Cut Algorithm for the Resolution of Large-Scale Symmetric Traveling Salesman Problems
An algorithm is described for solving large-scale instances of the Symmetric Traveling Salesman Problem (STSP) to optimality. The core of the algorithm is a “polyhedral” cutting-plane procedure that
Solving Large-Scale Symmetric Travelling Salesman Problems to Optimality
TLDR
The present study convincingly establishes the usefulness of mathematically proven good cutting-planes as an invaluable algorithmic tool for difficult combinatorial optimization problems.
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