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Multi-Objective Evolutionary Algorithms for Sensor Network Design

Multi-Objective Evolutionary Algorithms for Sensor Network Design

Ramesh Rajagopalan, Chilukuri K. Mohan, Kishan G. Mehrotra, Pramod K. Varshney
Copyright: © 2008 |Pages: 31
ISBN13: 9781599044989|ISBN10: 1599044986|ISBN13 Softcover: 9781616926878|EISBN13: 9781599045009
DOI: 10.4018/978-1-59904-498-9.ch008
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MLA

Rajagopalan, Ramesh, et al. "Multi-Objective Evolutionary Algorithms for Sensor Network Design." Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, IGI Global, 2008, pp. 208-238. https://doi.org/10.4018/978-1-59904-498-9.ch008

APA

Rajagopalan, R., Mohan, C. K., Mehrotra, K. G., & Varshney, P. K. (2008). Multi-Objective Evolutionary Algorithms for Sensor Network Design. In L. Thu Bui & S. Alam (Eds.), Multi-Objective Optimization in Computational Intelligence: Theory and Practice (pp. 208-238). IGI Global. https://doi.org/10.4018/978-1-59904-498-9.ch008

Chicago

Rajagopalan, Ramesh, et al. "Multi-Objective Evolutionary Algorithms for Sensor Network Design." In Multi-Objective Optimization in Computational Intelligence: Theory and Practice, edited by Lam Thu Bui and Sameer Alam, 208-238. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-498-9.ch008

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Abstract

Many sensor network design problems are characterized by the need to optimize multiple conflicting objectives. However, existing approaches generally focus on a single objective (ignoring the others), or combine multiple objectives into a single function to be optimized, to facilitate the application of classical optimization algorithms. This restricts their ability and constrains their usefulness to the network designer. A much more appropriate and natural approach is to address multiple objectives simultaneously, applying recently developed multi-objective evolutionary algorithms (MOEAs) in solving sensor network design problems. This chapter describes and illustrates this approach by modeling two sensor network design problems (mobile agent routing and sensor placement), as multi-objective optimization problems, developing the appropriate objective functions and discussing the tradeoffs between them. Simulation results using two recently developed MOEAs, viz., EMOCA (Rajagopalan, Mohan, Mehrotra, & Varshney, 2006) and NSGA-II (Deb, Pratap, Agarwal, & Meyarivan, 2000), show that these MOEAs successfully discover multiple solutions characterizing the tradeoffs between the objectives.

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