Modern heuristic techniques for combinatorial problems
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Tabu Search
Multilevel cooperative search: application to the circuit/hypergraph partitioning problem
ISPD '00 Proceedings of the 2000 international symposium on Physical design
Cooperative Parallel Tabu Search for Capacitated Network Design
Journal of Heuristics
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A first multilevel cooperative algorithm for capacitated multicommodity network design
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Explicit and Emergent Cooperation Schemes for Search Algorithms
Learning and Intelligent Optimization
Parallel tabu search for optimizing the OSPF weight setting problem
WSEAS TRANSACTIONS on COMMUNICATIONS
A comparison of problem decomposition techniques for the FAP
Journal of Heuristics
A taxonomy of cooperative search algorithms
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
A multilevel algorithm for large unconstrained binary quadratic optimization
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Cooperative search for fair nurse rosters
Expert Systems with Applications: An International Journal
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Cooperative search is a parallelization strategy for search algorithms where parallelism is obtained by concurrently executing several search programs. The solution space is implicitly decomposed according to the search strategy of each program. The programs cooperate by exchanging information on previously explored regions of the solution space. In this paper we propose a new design for cooperative search algorithms which is also a new parallel problem solving paradigm for combinatorial optimization problems. Our new design is based on an innovative approach to decompose the solution space which is inspired from the modeling of cooperative algorithms based on dynamical systems theory. Our design also gives a new purpose to the sharing of information among cooperating tasks based on principles borrowed from scatter search evolutionary algorithms. We have applied this paradigm to the graph partitioning problem. We describe the parallel implementation of this algorithm on a cluster of workstations and compare our results with other well known graph partitioning methods.