Modern heuristic techniques for combinatorial problems
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Introduction to Constraint Logic Programming
Introduction to Constraint Logic Programming
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Journal of Heuristics
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ACM Computing Surveys (CSUR)
Ant Colony Optimization
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
A hybrid approach for the 0-1 multidimensional knapsack problem
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Combinations of local search and exact algorithms
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Hybrid Metaheuristics: An Emerging Approach to Optimization
Hybrid Metaheuristics: An Emerging Approach to Optimization
Ant Colony Optimization and Constraint Programming
Ant Colony Optimization and Constraint Programming
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Large neighbourhood search algorithms for the founder sequence reconstruction problem
Computers and Operations Research
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HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Improved algorithms for inferring the minimum mosaic of a set of recombinants
CPM'07 Proceedings of the 18th annual conference on Combinatorial Pattern Matching
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This article is about a tutorial on hybrid metaheuristics which was given at the first edition of the conference Theory and Practice of Natural Computing, held in October 2012 in Tarragona, Spain. Hybrid metaheuristics are techniques for (combinatorial) optimization that result from a combination of algorithmic components originating from different optimization methods. The tutorial covers five representative examples: (1) the extension of iterated local search towards population-based optimization, (2) the introduction of elements from constraint programming into ant colony optimization, (3) the integration of branch & bound into variable neighborhood search, (4) the use of problem relaxation for guiding tabu search, and (5) the combination of dynamic programming with evolutionary algorithms.