Caching in the TSP Search Space

Heuristic search techniques can often benefit from record keeping and saving of intermediate results, thereby improving performance through exploitation of time / space tradeoffs. Iterative hill climbing (ITHC) is one of these heuristics. This paper demonstrates that record keeping in the ITHC domain can significantly speed up the ITHC. The record keeping method is similar to the mechanism of a cache. The new approach is implemented and tested in the traveling salesperson search space. The research compares a traditional random restart (RR) procedure to a new greedy enumeration (GE) method. GE produces Hamiltonian-cycles that are about 10% shorter than the RR. Moreover, the cached RR achieves a speedup of 3x with a relatively small number of cities and only 20% with a medium number of cities (~17). The cached GE shows a highly significant speedup of 4x over traditional methods even with a relatively large number of cities (>80).

[1]  William J. Cook,et al.  The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics) , 2007 .

[2]  Fabio Gagliardi Cozman,et al.  Anytime anyspace probabilistic inference , 2004, Int. J. Approx. Reason..

[3]  David S. Johnson,et al.  The Traveling Salesman Problem: A Case Study in Local Optimization , 2008 .

[4]  Robert Bernecky Book review: Multiprocessors by Daniel Tabak (Prentice Hall, Englewood Cliffs, NJ) , 1991, CARN.

[5]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[6]  William J. Cook,et al.  The Traveling Salesman Problem: A Computational Study , 2007 .

[7]  Toniann Pitassi,et al.  An Exponential Time/Space Speedup For Resolution , 2007, Electron. Colloquium Comput. Complex..

[8]  Bowei Xi,et al.  A smart hill-climbing algorithm for application server configuration , 2004, WWW '04.

[9]  David Allen,et al.  Optimal Time-Space Tradeoff in Probabilistic Inference , 2003, Probabilistic Graphical Models.

[10]  Shlomo Zilberstein,et al.  Using Anytime Algorithms in Intelligent Systems , 1996, AI Mag..

[11]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[12]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[13]  Tandy J. Warnow,et al.  Better Hill-Climbing Searches for Parsimony , 2003, WABI.

[14]  Judea Pearl,et al.  Heuristics : intelligent search strategies for computer problem solving , 1984 .

[15]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.

[16]  Armand M. Makowski,et al.  Comparing strength of locality of reference - popularity, majorization, and some folk theorems , 2004, IEEE INFOCOM 2004.

[17]  Geoffrey J. Gordon,et al.  A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games , 2007, AISTATS.