Complete Local Search with Memory
Journal of Heuristics
Meta-heuristics: The State of the Art
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Solving the uncapacitated facility location problem using tabu search
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
MAGMA: a multiagent architecture for metaheuristics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a coalition-based metaheuristic (CBM) to solve the uncapacitated facility location problem. CBM is a population-based metaheuristic where individuals encapsulate a single solution and are considered as agents. In comparison to classical evolutionary algorithms, these agents have additional capacities of decision, learning and cooperation. Our approach is also a case study to present how concepts from multiagent systems' domain may contribute to the design of new metaheuristics. The tackled problem is a well-known combinatorial optimization problem, namely the uncapacitated facility location problem, that consists in determining the sites in which some facilities must be set up to satisfy the requirements of a client set at minimum cost. A computational experiment is conducted to test the performance of learning mechanisms and to compare our approach with several existing metaheuristics. The results showed that CBM is competitive with powerful heuristics approaches and presents several advantages in terms of flexibility and modularity.