Tools
Tools
Cao, B, Zhao, J, Yang, P, Lv, Z, Liu, X, Kang, X, Yang, S and Kang, K (2017) Distributed Parallel Cooperative Coevolutionary Multi-Objective Large-Scale Immune Algorithm for Deployment of Wireless Sensor Networks. Future Generation Computer Systems. ISSN 0167-739X
|
Text
CSAs (3).pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
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 deployment of a wireless sensor network, which is a self-organization network. In experiments, compared with several state-of-the-art multi-objective evolutionary algorithms the Cooperative Coevolutionary Generalized Differential Evolution 3, the Cooperative Multi-objective Differential Evolution and the Nondominated Sorting Genetic Algorithm III, the proposed algorithm addresses the deployment optimization problem efficiently and effectively.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0805 Distributed Computing, 0806 Information Systems |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Elsevier |
| Date Deposited: | 16 Oct 2017 11:23 |
| Last Modified: | 04 Sep 2021 11:06 |
| DOI or ID number: | 10.1016/j.future.2017.10.015 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/7362 |
 |
View Item |