An adaptive variable neighborhood search for solving the multi-objective node placement problem

In this paper, we address the multi-objective node placement (MONP) problem that consists in extending an existing heterogeneous network while optimizing three conflicting objectives. Numerous devices' types are to be deployed in an area of interest in order to ensure the network connectivity and to satisfy users' demands. As the MONP problem is NP-Hard, we adapt a new variant of the multiobjective variable neighborhood search (MO-VNS) algorithm. The main idea is to automatically choose the most promising neighborhood structure by associating a rating to each one. This value is dynamically updated according to the number of so far generated non-dominated solutions. The empirical validation is done using a simulation environment called Inform Lab. A comparison to an existing multi-objective genetic approach is performed based on real instances of maritime surveillance application. The experimental results show that both methods have comparable performances based the unary hyper-volume metric . A slight improvement is noticed for the (MO-VNS) regarding medium sized instances.