Multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints

Authors:
Erfu Yang;Ahmet T. Erdogan;Tughrul Arslan;Nick H. Barton
Affiliations:
The University of Edinburgh, School of Engineering and Electronics, King’s Buildings, EH9 3JL, Edinburgh, UK;The University of Edinburgh, School of Engineering and Electronics, King’s Buildings, EH9 3JL, Edinburgh, UK;The University of Edinburgh, School of Engineering and Electronics, King’s Buildings, EH9 3JL, Edinburgh, UK;The University of Edinburgh, School of Biological Sciences, King’s Buildings, EH9 3JL, Edinburgh, UK
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
Year:
2011

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Accelerated evolution: a biologically-inspired approach for augmenting self-star properties in wireless sensor networks

Transactions on Computational Science XV

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Abstract

Wireless sensor networks have emerged as a promising way to develop high security systems. This paper presents the optimizations of a space-based reconfigurable sensor network under hard constraints by employing an efficient multi-objective evolutionary algorithm (MOEA). First, a system model is proposed for cluster-based space wireless sensor networks. Second, the statement of multi-objective optimization problems is mathematically formulated under hard constraints. Third, the MOEA is used to find multi-criteria solutions in the sense of Pareto optimality. Finally, simulation results are provided to illustrate the effectiveness of applying the MOEA to the multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints.