A study of diversification strategies for the quadratic assignment problem
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
Reactive search, a history-sensitive heuristic for MAX-SAT
Journal of Experimental Algorithmics (JEA)
Tabu Search
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Breakout Local Search for maximum clique problems
Computers and Operations Research
A study of breakout local search for the minimum sum coloring problem
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Breakout Local Search for the Max-Cutproblem
Engineering Applications of Artificial Intelligence
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We investigate the contribution of a recently proposed adaptive diversification strategy (ADS) to the performance of an iterated local search (ILS) algorithm. ADS is used as a diversification mechanism by breakout local search (BLS), which is a new variant of the ILS metaheuristic. The proposed perturbation strategy adaptively selects between two types of perturbations (directed or random moves) of different intensities, depending on the current state of search. We experimentally evaluate the performance of ADS on the quadratic assignment problem (QAP) and the maximum clique problem (MAX-CLQ). Computational results accentuate the benefit of combining adaptively multiple perturbation types of different intensities. Moreover, we provide some guidance on when to introduce a weaker and when to introduce a stronger diversification into the search.