A Visual Method for Analysis and Comparison of Search Landscapes

Combinatorial optimization problems and corresponding (meta-)heuristics have received much attention in the literature. Especially, the structural or topological analysis of search landscapes is important for evaluating the applicability and the performance of search operators for a given problem. However, this analysis is often tedious and usually the focus is on one specific problem and only a few operators. We present a visual analysis method that can be applied to a wide variety of problems and search operators. The method is based on steepest descent walks and shortest distances in the search landscape. The visualization shows the search landscape as seen by the search algorithm. It supports the topological analysis as well as the comparison of search landscapes. We showcase the method by applying it to two different search operators on the TSP, the QAP, and the SMTTP. Our results show how differences between search operators manifest in the search landscapes and how conclusions about the suitability of the search operator for different optimizations can be drawn.

[1]  Michael C. Hout,et al.  Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.

[2]  Martin Middendorf,et al.  Visualizing Topological Properties of the Search Landscape of Combinatorial Optimization Problems , 2015 .

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

[4]  David M. McCandlish,et al.  VISUALIZING FITNESS LANDSCAPES , 2011, Evolution; international journal of organic evolution.

[5]  Franz Rendl,et al.  QAPLIB – A Quadratic Assignment Problem Library , 1997, J. Glob. Optim..

[6]  Susan Khor,et al.  Search space analysis with Wang-Landau sampling and slow adaptive walks , 2011, ArXiv.

[7]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[8]  Michael Affenzeller,et al.  A Comprehensive Survey on Fitness Landscape Analysis , 2012, Recent Advances in Intelligent Engineering Systems.

[9]  L. Kallel,et al.  How to detect all maxima of a function , 2001 .

[10]  G. Croes A Method for Solving Traveling-Salesman Problems , 1958 .

[11]  David S. Johnson,et al.  Local Optimization and the Traveling Salesman Problem , 1990, ICALP.

[12]  P. Stadler,et al.  The landscape of the traveling salesman problem , 1992 .

[13]  Martin Middendorf,et al.  Comparing the Optimization Behaviour of Heuristics with Topology Based Visualization , 2014, TPNC.

[14]  Mark Bailey,et al.  The Grammar of Graphics , 2007, Technometrics.

[15]  Gerik Scheuermann,et al.  dPSO‐Vis: Topology‐based Visualization of Discrete Particle Swarm Optimization , 2013, Comput. Graph. Forum.

[16]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[17]  David Thomas,et al.  The Art in Computer Programming , 2001 .

[18]  Fred Glover,et al.  Improved Constructive Multistart Strategies for the Quadratic Assignment Problem Using Adaptive Memory , 1999, INFORMS J. Comput..

[19]  Martin Middendorf,et al.  Visual Analysis of Discrete Particle Swarm Optimization Using Fitness Landscapes , 2014 .

[20]  Adam Prügel-Bennett,et al.  Large barrier trees for studying search , 2005, IEEE Transactions on Evolutionary Computation.

[21]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[22]  Kenneth Dean Boese,et al.  Models for iterative global optimization , 1996 .

[23]  Michael T. Wolfinger,et al.  Barrier Trees of Degenerate Landscapes , 2002 .

[24]  Thomas Stützle,et al.  A review of metrics on permutations for search landscape analysis , 2007, Comput. Oper. Res..

[25]  P. Preux,et al.  Fitness Landscapes of Combinatorial Problems and the Performance of Search Algorithms , 1997 .

[26]  Cyril Fonlupt,et al.  Fitness Landscapes and Performance of Meta-Heuristics , 1999 .

[27]  Urska Cvek,et al.  High-Dimensional Visualizations , 2002 .

[28]  Steven Halim,et al.  Viz: a visual analysis suite for explaining local search behavior , 2006, UIST.

[29]  P. Stadler,et al.  Combinatorial vector fields and the valley structure of fitness landscapes , 2010, Journal of mathematical biology.

[30]  W. Marsden I and J , 2012 .