Multi-level cooperative search: A new paradigm for combinatorial optimization and an application to graph partitioning

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.