Iterated Local Search (ILS) for the Quadratic Assignment Problem (QAP) Tim Daniëlse en Vincent...
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Iterated Local Search (ILS) for the Quadratic Assignment Problem (QAP)
Tim Daniëlse en Vincent Delvigne
QAP
• facilities (items)• locations• distancematrix A (between locations)• flowmatrix B (between facilities)• Assign facilities to locations, minimizing
transportation needed.
QAP (2)
• is the distance between locations and • is the flow between facilties and • is the set of all permutations of • gives the location of item in solution
Complexity
• NP-hard• Largest non-trivial instance solved to
optimality: • Thus use heuristics:– Simulated annealing– Tabu search– Memetic algorithms– Ant algorithms– Scatter search
Iterated Local Search
Procedure Iterated Local SearchGenerateInitialSolution LocalSearch()
repeat Perturbation() LocalSearch(’) AcceptanceCriterion()
Until termination condition metEndProcedure
Iterated Local Search (2)
• Generate Initial Solution– No known, well performing construction algorithm– Randomized assignment
Iterated Local Search (3)
• Local Search– 2-opt– First-improvement pivot
– Don’t look bits
Iterated Local Search (4)
• Perturbation– random k-opt– best value of k not known a priori– Adaptive, between and
Iterated Local Search (5)
• Acceptance Criterion– standard criterion: accept only improvements– varies among the algorithm-variants
QAP instance classes
• QAPLIB benchmark library• 4 instance classes:– randomly generated (class i)– Manhattan distance matrix (class ii)– real-life instances (class iii)– random, resembling real-life (class iv)
QAP instance classes (2)
• Functions to differentiate amongst classes:- Flow dominance
- Distance dominance
- Sparsity
where is number of “0” entries in A or B
Analysis of search space
• Fitness-Distance correlation analysis
• 5000 LS runs• 1000 ILS runs with iterations.
Analysis of search space (2)
Analysis of search space (3)
Distance-Fitness Correlation
Distance-Fitness Correlation (2)
Stagnation Detection• Empirical run-time
distribution (RTD)• RTD develops below
exponential distribution (stagnation)– perform restart
Algorithm Variations
• Soft Restarts– Use of history– Random new solution.
• Random Walk (RW)– Accept answer regardless of improvement– Combination with default “Better” might improve
even more
Algorithm Variations (2)
• Large Step Markov Chains (LSMC)– Accepts worse solution with certain probability.– Similar to simulated annealing – Uses temperature parameter
Algorithm Variations (3)
• Population Based Extensions• Replace-Worst– Start with solutions each with standard ILS– Every iterations a copy of the best replaces the
worst
Algorithm Variations (4)
• Evolution strategies (ES)– Population of solutions– Each iteration new solutions are generated– Uses distance to determine membership of
population– Parameter
Algorithm Comparison
• Robust Tabu search (RoTS)– Best for class and
• Max-Min Ant System (MMAS)– Best for class and
Algorithm Comparison (2)
Algorithm Comparison (3)
Evolutionary Variant
• Variant of the Evolutionary Strategy.
• Optimized Local Search• Different parameter settings
Evolutionary Variant (3)
Conclusions
• Fitness-Distance Correlation analysis• ILS runtime analysis• Acceptance Criteria analysis• ES-MN best performing algorithm.
References
• Stutzle, Thomas (2006): Iterated Local Search for the Quadratic Assignment Problem. European Journal of Operational Research 174 (3), 1519-1539.