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

摘要

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.