1. Tuning Tabu Search Strategies via Visual Diagnosis >MIC2005<<Vienna< 6 th Metaheuristics International Conference August 22-26, 2005. Vienna, Austria By: Lau Hoong Chuin, Wan Wee Chong, and Steven Halim (Presenter)

2. Outline Introduction (The Problem) –Metaheuristics Tuning Problem (illustrated using Tabu Search) Visual Diagnosis Tuning (The Methodology) –Human + Computer –{Cause – Action – Outcome} tuple V-MDF (Visualizer for MDF) (The Tool) –Distance Radar –V-MDF Architecture –Experimental Results Questions & Answers

3. Introduction: Tuning Problem Characteristics of a practical metaheuristic: –Delivers high quality solutions for any future instances. –Run in reasonable running time. –Can be developed within tight development time. The need for a proper tuning. Taxonomy of Tuning Problem: –Static versus Dynamic –Three levels of complexity Tuning is complex… (illustrated using Tabu Search) Static Level-3 Tuning Search Strategies Level-2 Choosing Best Configuration Level-1 Calibrating Parameter Values Dynamic Tuning Problems

4. Tuning Tabu Search Level-1 Tuning Problem (Static) (Calibrating Parameter Values) Setting the length of Tabu tenure: –By Guessing ?? –By Trial and Error ?? –By using past experience as a rough guide ?? Static Level-3 Tuning Search Strategies Level-2 Choosing Best Configuration Level-1 Calibrating Parameter Values Dynamic Tuning Problems

5. Tuning Tabu Search Level-2 Tuning Problem (Static) (Choosing the best Configuration) Choices of Local Neighborhood: –2-opt ?? –3-opt ?? –Very Large Scale Neighborhood (VLSN) ?? Choices of Tabu List: –Tabu moves ?? –Tabu attributes ?? –Tabu solutions ?? Static Level-3 Tuning Search Strategies Level-2 Choosing Best Configuration Level-1 Calibrating Parameter Values Dynamic Tuning Problems

6. Tuning Tabu Search Level-3 Tuning Problem (Dynamic) (Tuning Search Strategies) Choices of Search Strategies: –Intensification ?? –Diversification ?? –Hybridization ?? Example: –Reactive Tabu Search (Battiti & Tecchiolli, 1994) When and How to apply these strategies ?? Static Level-3 Tuning Search Strategies Level-2 Choosing Best Configuration Level-1 Calibrating Parameter Values Dynamic Tuning Problems

7. Bottleneck !!! Conventional Solution Implement the metaheuristic Evaluate its performance Good or Give up Not good Stop Modify the metaheuristic using past knowledge, past experiences, plus some instinct blindly. (“Blind trial-and-error”) Tuning: bottleneck in rapid development process (Adenso-Diaz & Laguna, 2005)

8. Automated Tuning Methods Tool to automatically and systematically search for the best: Set of parameter values(level-1) Configuration(level-2) Pros: Relieves the burden of tuning from human. Cons: Treat metaheuristic as a black box. –Does not provide room for innovations… Difficult to address level-3 Tuning Problem (for Dynamic Metaheuristic) Probably slow –if the number of possible configurations is high. Examples: CALIBRA (Adenso-Diaz & Laguna, 2005) F-Race (Birattari, 2004)

9. Non-Automated Tuning Methods Tool which allow human to diagnose the metaheuristic Pros: Make level-3 Tuning Problem for Dynamic Metaheuristic easier Provide room for innovations… Cons: Human still need to do the job… Inconsistent results Examples: Statistical Analysis, e.g.: –Fitness Landscape Analysis (Fonlupt et al, 1997) –Fitness Distance Correlation Analysis (Merz, 2000) Human-Guided Tabu Search (Klau et al, 2002) Visualization of Search (Kadluczka et al, 2004) V-MDF (This work)

10. Visual Diagnosis Tuning The methodology for solving Tuning Problem V-MDF The tool to support Visual Diagnosis Tuning

11. Visual Diagnosis Tuning Idea: Combine human intelligence and computer to produce good search strategies quickly. Basic methodology of Visual Diagnosis Tuning: – {Cause-Action-Outcome} tuple: Diagnose incidents in search trajectory.(Cause) Steer the search if necessary. (Action) Instantly observe the impact of the his action.(Outcome) –Example: {passive searching – greedy random restart – arrive in good region} –Possibly an effective strategy {solution cycling – decrease tabu tenure – solution cycling} –Possibly an ineffective strategy

12. V-MDF: Distance Radar Diagnose incidents in search trajectory –Visualizing search trajectory is difficult! Search space is large! A special generic visualizer is needed: Distance Radar –Using the concept of “distance” Example: Distance between two binary encoded solutions: hamming distance. A = 110010 B = 100011 Distance = 2 bit flips.

13. V-MDF: Distance Radar Main ideas of Distance Radar: –Record elite solutions along the search trajectory. Distance w.r.tCurrent solution Recency w.r.tCurrent iteration Objective Valuew.r.tCurrent best objective value –Current solution  current position. –Elite solutions (Local Optimal)  anchor points. –Approximate Tabu Search trajectory with these information.

14. V-MDF: Radar A Distance Radar consists of Radar A and B. This is Radar A. X-axis: Local Optimal Y-axis: Distance Plot distance and recency of these local optimal against current solution and current interation Distance Information in Logarithmic scale This is a Recency Graph to augment Radar A X-axis: Local Optimal Y-axis: Recency Current solution is close to these elite solutions and they are recent. Interpretation: exploring good region In Radar A, elite solutions are sorted by Objective Value

15. V-MDF: Radar B This is Radar B. It portrays distance information from different angle. X-axis: Local Optimal Y-axis: Distance Plot distance and obj value of these local optimal against current solution and best so far This is an Objective Value Graph to augment Radar B X-axis: Local Optimal Y-axis: Objective Value In Radar B, elite solutions are sorted by Recency No cycling, objective value fluctuates. Interpretation: Tabu Search is working correctly at the moment.

16. V-MDF: Distance Radar Radar A, B, Recency and Objective Value Graph can be used together to draw more information about the search trajectory

17. V-MDF: Remedial Actions Series of non-improving moves observed…and it requires remedial action For intensification, this is one of the correct trajectory For Diversification, this is one of the correct trajectory

18. Rules selection phase Visual Diagnosis Tuning Phase V-MDF: Overall Architecture Implement the metaheuristic in MDF framework (Lau et al, 2004) See also TSF in Metaheuristics: Progress as Real Problem Solvers. Diagnose the metaheuristic against training instances using Distance Radar Automatic extraction of Good Rules from Knowledge Base to form the final metaheuristic algorithm Add Rules to Knowledge Base Apply the metaheuristic with good rules to whole test instances

19. Experiment using V-MDF Task: –Tune a Tabu Search implementation for solving an NP-hard Military Transport Planning (MTP) problem. Knowledge base of rules after training. Poor rules are discarded… Good rules form the final metaheuristic algorithm

20. Experiment using V-MDF The results of a training instance (minimizing problem)

21. Experiment using V-MDF Tabu Search results

22. Summary The Tuning Problem. (The Problem) –Taxonomy of tuning problems: Static vs Dynamic & 3 levels of complexity. –Current tuning methods: Automated vs Non-automated Visual Diagnosis Tuning (The Methodology) –{Cause-Action-Outcome}. Visualizer for MDF (V-MDF) (The Tool) –Distance Radar and its usage. –Overview of V-MDF –Generic (not restricted to one problem). –Useful especially for new problems.

23. Questions & Answers Thank you for your attention My e-mail: stevenha@comp.nus.edu.sg