A new adaptive multi-start technique for combinatorial global optimizations

Operations Research Letters

Volumn 16, Issue 2, 1994, Pages 101-113

  Boese, Kenneth D ^a   Kahng, Andrew B ^a   Muddu, Sudhakar ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Global optimization; Graph bisection; Heuristic search; Multi start; Stochastic hill climbing; Traveling salesman problem

Indexed keywords

EID: 0000692965     PISSN: 01676377     EISSN: None     Source Type: Journal    
DOI: 10.1016/0167-6377(94)90065-5     Document Type: Article

References (27)

1. Ultrametricity, frustration and the graph colouring problem
2. Toward practical ‘neural’ computation for combinatorial optimization problems
3. Fast algorithms for geometric traveling salesman problems
4. Best-so-far vs. where-you-are: implications for optimal finite-time annealing
5. On the big valley and adaptive multi-start for discrete global optimizations
6. Graph bisection algorithms with good average case behavior
7. Optimization of globally convex functions
8. Local optimization and the traveling salesman problem
9. Optimization by simulated annealing: an experimental evaluation, Part I, graph partitioning
10. Toward a general theory of adaptive walks on rugged landscapes
11. Optimization by simulated annealing
12. Configuration space analysis of travelling salesman problems
13. A replica analysis of the travelling salesman problem
14. Parallel genetic algorithms, population genetics and combinatorial optimization
15. Evolution algorithms in combinatorial optimization
16. The Complexity of the Lin–Kernighan Heuristic for the Traveling Salesman Problem
17. Configuration space analysis for optimization problems
18. Efficient simulated annealing on fractal energy landscapes
19. Statistical mechanics and the traveling salesman problem
20. Genetic local search algorithms for the traveling salesman problem
21. A two-level two-way-partitioning algorithm
22. Correlated and uncorrelated fitness landscapes and how to tell the difference