Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

The use of immune algorithms is generally a time-intensive process—especially for problems with numerous variables. In the present paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm parallelized utilizing the message passing interface (MPI). The proposed algorithm comprises three layers: objective, group and individual layers. First, to tackle each objective in a multi-objective problem, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives simultaneously. Second, the numerous variables are divided into several groups. Finally, individual evaluations are allocated across many core processing units, and calculations are performed in parallel. Consequently, the computation time is greatly reduced. The proposed algorithm integrates the idea of immune algorithms, exploring sparse areas in the objective space, and uses simulated binary crossover for mutation. The proposed algorithm is employed to optimize the 3D terrain deployment of a wireless sensor network, which is a self-organization network. In our experiments, through comparisons with several state-of-the-art multi-objective evolutionary algorithms—the cooperative coevolutionary generalized differential evolution 3, the cooperative multi-objective differential evolution, the multi-objective evolutionary algorithm based on decision variable analyses and the nondominated sorting genetic algorithm III—the proposed algorithm addresses the deployment optimization problem efficiently and effectively.