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

Abstract

Using immune algorithms is generally a time-intensive process-especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large number of 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, which tend to explore sparse areas in the objective space and use simulated binary crossover for mutation. The proposed algorithm is employed to optimize the 3D terrain

Multiobjective 3-D Topology Optimization of Next-Generation Wireless Data Center Network

“…For the distributed MOEAs, we select the following: Distributed Parallel Cooperative Coevolutionary Multi-Objective Evolutionary Algorithm (DPCCMOEA) [24], Distributed Parallel Cooperative Coevolutionary Multi-Objective Evolutionary Algorithm (DPCCMOLSEA) [25]. and Distributed Parallel Cooperative Coevolutionary Multi-Objective Large-Scale Immune Algorithm (DPCCMOLSIA) [26]. The summary of all parallel algorithms is listed in Table II.…”

Node deployment in wireless sensor networks using the new multi‐objective Levy flight bee algorithm

“…Due to the great popularity of the WSN field, especially in the deployment issue, there are a large number of related researches; therefore, the most recent studies have been noted. Cao et al [12] applied a distributed parallel cooperative co-evolutionary multi-objective large-scale immune algorithm, which utilise the message passing interface (MPI) to optimise the 3D terrain deployment of a WSN. In [13] Cao et al applied two-particle swarm optimisers, the cooperative co-evolutionary PSO 2 (CCPSO2) and the comprehensive learning particle swarm optimiser (CLPSO), to maximise coverage and prolong lifetime.…”

Differential Evolution in Wireless Communications: A Review

“…Different examples are given. They are: the case of heterogenous networks consisting of WiFi access points [123]; multi objective node deployment to ensure reliable and efficient real time performance [124][125] and lifetime maximisation [126]; optimum allocation of spectrum in wireless networks [127] and minimisation of the number of links in WSNs [128].…”