Exact and heuristic methods for solving difficult optimiza- tion problems have been usually considered as two completely different approaches, with each of them being extensively studied by a different research community and used in different fields of application. In this work we propose a scheme for the integration and cooperation of an exact method, based on mixed-integer linear programming (MILP), and a bio-inspired metaheuristic, a genetic algorithm (GA), for the solution of a relay node placement problem in wireless sensor networks. The integration aspect relates to the use of a MILP solver inside GA's operators. The cooperation aspect is implemented through a shared incumbent environment in which a MILP solver and the GA work in par- allel and exchange relevant information in order to overcome their indi- vidual weaknesses and provide better solutions within a shorter time. Experimental results show a significant increase of performance when integration and cooperation take place, in comparison to the performance of the MILP and the GA when used independently from each other.
['Eduardo Feo Flushing', 'Gianni A. Di Caro']
Exploiting Synergies Between Exact and Heuristic Methods in Optimization: An Application to the Relay Placement Problem in Wireless Sensor Networks