The node placement problem involves positioning and configuring infrastructure for wireless networks. Applied to next generation networks, it establishes a new wireless architecture able to integrate heterogeneous components that can collaborate and exchange data. Furthermore, the heterogeneity of wireless networks makes the problem more intractable. This paper presents a novel multi-objective node placement problem that optimizes concurrently four objectives: maximizing communication coverage, minimizing the active structures’ costs, maximizing of the total capacity bandwidth and minimizing the noise level in the network. Known to be NP -hard, the problem can be approached by applying heuristics mainly for large problem instances. As the number of nodes to place is not determined beforehand; we propose to apply a multi-objective variable-length genetic algorithm (VLGA) that simultaneously searches for the optimal number, positions and nature of heterogeneous nodes and communication devices. The performance of the VLGA is highlighted through the implementation of a decision support system (DSS) applied to the surveillance maritime problem using real data instances. We compare the ability of the proposed algorithm with an existing multi-objective model from the literature in order to validate its effectiveness in dealing with heterogeneous components. The results show that the proposed model well fits the network architecture constraints with a better balance between the objectives applied to the surveillance problem. 
A genetic algorithm based decision support system for the multi-objective node placement problem in next wireless generation network