Multi-objective evolutionary algorithms for sensor network design

Ramesh Rajagopalan, Chilukuri K Mohan, Kishan G. Mehrotra, Pramod Kumar Varshney

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationMulti-Objective Optimization in Computational Intelligence: Theory and Practice
PublisherIGI Global
Pages208-238
Number of pages31
ISBN (Print)9781599044989
DOIs
StatePublished - 2008

Fingerprint

Evolutionary algorithms
Sensor networks
Mobile agents
Multiobjective optimization
Sensors

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Rajagopalan, R., Mohan, C. K., Mehrotra, K. G., & Varshney, P. K. (2008). Multi-objective evolutionary algorithms for sensor network design. In 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

Multi-objective evolutionary algorithms for sensor network design. / Rajagopalan, Ramesh; Mohan, Chilukuri K; Mehrotra, Kishan G.; Varshney, Pramod Kumar.

Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global, 2008. p. 208-238.

Research output: Chapter in Book/Report/Conference proceedingChapter

Rajagopalan, R, Mohan, CK, Mehrotra, KG & Varshney, PK 2008, Multi-objective evolutionary algorithms for sensor network design. in Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global, pp. 208-238. https://doi.org/10.4018/978-1-59904-498-9.ch008
Rajagopalan R, Mohan CK, Mehrotra KG, Varshney PK. Multi-objective evolutionary algorithms for sensor network design. In Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global. 2008. p. 208-238 https://doi.org/10.4018/978-1-59904-498-9.ch008
Rajagopalan, Ramesh ; Mohan, Chilukuri K ; Mehrotra, Kishan G. ; Varshney, Pramod Kumar. / Multi-objective evolutionary algorithms for sensor network design. Multi-Objective Optimization in Computational Intelligence: Theory and Practice. IGI Global, 2008. pp. 208-238
@inbook{f6e7b902b304446f919c97af36ca8a73,
title = "Multi-objective evolutionary algorithms for sensor network design",
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.",
author = "Ramesh Rajagopalan and Mohan, {Chilukuri K} and Mehrotra, {Kishan G.} and Varshney, {Pramod Kumar}",
year = "2008",
doi = "10.4018/978-1-59904-498-9.ch008",
language = "English (US)",
isbn = "9781599044989",
pages = "208--238",
booktitle = "Multi-Objective Optimization in Computational Intelligence: Theory and Practice",
publisher = "IGI Global",

}

TY - CHAP

T1 - Multi-objective evolutionary algorithms for sensor network design

AU - Rajagopalan, Ramesh

AU - Mohan, Chilukuri K

AU - Mehrotra, Kishan G.

AU - Varshney, Pramod Kumar

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=70349695172&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349695172&partnerID=8YFLogxK

U2 - 10.4018/978-1-59904-498-9.ch008

DO - 10.4018/978-1-59904-498-9.ch008

M3 - Chapter

SN - 9781599044989

SP - 208

EP - 238

BT - Multi-Objective Optimization in Computational Intelligence: Theory and Practice

PB - IGI Global

ER -