Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Computers and Operations Research
A Constraint-Based Approach for Examination Timetabling Using Local Repair Techniques
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Case-based heuristic selection for timetabling problems
Journal of Scheduling
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Fuzzy multiple heuristic orderings for examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
A new adaptive multi-start technique for combinatorial global optimizations
Operations Research Letters
Multi-agent resource allocation (MARA) for modeling construction processes
Proceedings of the 40th Conference on Winter Simulation
Computers and Operations Research
Adaptive memory-based local search for MAX-SAT
Applied Soft Computing
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In this article, we introduce a new solving framework based on using alternatively two local-search algorithms to solve constraint satisfaction and optimization problems. The technique presented is based on the integration of local-search algorithm as a mechanism to diversify the search instead of using a build on diversification mechanisms. Thus, we avoid tuning the multiple parameters to escape from a local optimum. This technique improves the existing methods: it is generic especially when the given problem can be expressed as a constraint satisfaction problem. We present the way the local-search algorithm can be used to diversify the search in order to solve real examination timetabling problems. We describe how the local-search algorithm can be used to assist any other specific local-search algorithm to escape from local optimality. We showed that such framework is efficient on real benchmarks for timetabling problems.