PARALLEL GUIDED LOCAL SEARCH AND SOME PRELIMINARY EXPERIMENTAL RESULTS FOR CONTINUOUS OPTIMIZATION

This paper proposes a Parallel Guided Local Search (PGLS) framework for continuous optimization. In PGLS, several guided local search (GLS) procedures (agents) are run for solving the optimization problem. The agents exchange information for speeding up the search. For example, the information exchanged could be knowledge about the landscape obtained by the agents. The proposed algorithm is applied to continuous optimization problems. The preliminary experimental results show that the algorithm is very promising