A comparison of multiple objective evolutionary algorithms for solving the multi-objective node placement problem

The multi-objective node placement (MONP) problem involves the extension of an existing heterogeneous network while optimizing three conflicting objectives: maximizing the communication coverage, minimizing active nodes and communication devises costs, and maximizing of the total capacity bandwidth in the network. Multiple devices´ types are to be deployed in order to ensure networks´ heterogeneity. As the MONP problem is NP-Hard, heuristic approaches are necessary for large problem instances. In this paper, we compare the ability of three different sorting-based multiple objective genetic algorithms to find an optimal placement and connection between the potential placed nodes. The empirical validation is performed using a simulation environment called Inform Lab and based on real instances of maritime surveillance application. Results and discussion on the performance of the algorithms are provided.