Optimal solution of set covering/partitioning problems using dual heuristics
Management Science
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Parallel Ant Colonies for Combinatorial Optimization Problems
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
A Study of Some Properties of Ant-Q
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
A survey on parallel ant colony optimization
Applied Soft Computing
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The Set Covering Problem (SCP) represents an important class of NP-hard combinatorial optimization problems. Actually, the exact algorithms, such as branch and bound, don't find optimal solutions within a reasonable amount of time, except for small problems. Recently, the Ant systems (AS) were proposed for solving several combinatorial optimization problems. This paper presents a hybrid approach based on Ant Systems combined with a local search for solving the SCP. To validate the implemented approach, many tests have been realized on known benchmarks, and, an empirical adjustment of its parameters has been realized. To improve the performance of this algorithm, two parallel implementations are proposed.