Multi-Objective Evolutionary Algorithms for Sensor Network Design

Multi-Objective Evolutionary Algorithms for Sensor Network Design

Ramesh Rajagopalan (Syracuse University, USA), Chilukuri K. Mohan (Syracuse University, USA), Kishan G. Mehrotra (Syracuse University, USA) and Pramod K. Varshney (Syracuse University, USA)
DOI: 10.4018/978-1-59904-498-9.ch008
OnDemand PDF Download:


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.

Complete Chapter List

Search this Book:
Reviewer List
Table of Contents
Zbigniew Michalewicz
Lam Thu Bui, Sameer Alam
Chapter 1
Lam Thu Bui, Sameer Alam
This chapter is devoted to summarize common concepts related to multi-objective optimization (MO). An overview of “traditional” as well as CI-based... Sample PDF
An Introduction to Multi-Objective Optimization
Chapter 2
Konstantinos E. Parsopoulos, Michael N. Vrahatis
The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems... Sample PDF
Multi-Objective Particles Swarm Optimization Approaches
Chapter 3
Saku Kukkonen, Lampinen Jouni
Multi-objective optimization with Evolutionary Algorithms has been gaining popularity recently because its applicability in practical problems. Many... Sample PDF
Generalized Differential Evolution for Constrained Multi-Objective Optimization
Chapter 4
Luis V. Santana-Quintero, Noel Ramírez-Santiago, Carlos A. Coello Coello
This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this... Sample PDF
Towards a More Efficient Multi-Objective Particle Swarm Optimizer
Chapter 5
Licheng Jiao, Maoguo Gong, Wenping Ma, Ronghua Shang
The human immune system (HIS) is a highly evolved, parallel and distributed adaptive system. The information processing abilities of HIS provide... Sample PDF
Multi-Objective Optimization Using Artificial Immune Systems
Chapter 6
Seamus M. McGovern, Surendra M. Gupta
NP-complete combinatorial problems often necessitate the use of near-optimal solution techniques including heuristics and metaheuristics. The... Sample PDF
Lexicographic Goal Programming and Assessment Tools for a Combinatorial Production Problem
Chapter 7
Andrew Lewis, Sanaz Mostaghim, Marcus Randall
Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective... Sample PDF
Evolutionary Population Dynamics and Multi-Objective Optimisation Problems
Chapter 8
Ramesh Rajagopalan, Chilukuri K. Mohan, Kishan G. Mehrotra, Pramod K. Varshney
Many sensor network design problems are characterized by the need to optimize multiple conflicting objectives. However, existing approaches... Sample PDF
Multi-Objective Evolutionary Algorithms for Sensor Network Design
Chapter 9
Soo-Yong Shin, In-Hee Lee, Byoung-Tak Zhang
Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA... Sample PDF
Evolutionary Multi-Objective Optimization for DNA Sequence Design
Chapter 10
Giuseppe Ascia, Vincenzo Catania, Alessandro G. Di Nuovo, Maurizio Palesi, Davide Patti
Multi-Objective Evolutionary Algorithms (MOEAs) have received increasing interest in industry, because they have proved to be powerful optimizers.... Sample PDF
Computational Intelligence to Speed-Up Multi-Objective Design Space Exploration of Embedded Systems
Chapter 11
Jason Teo, Lynnie D. Neri, Minh H. Nguyen, Hussein A. Abbass
This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO)... Sample PDF
Walking with EMO: Multi-Objective Robotics for Evolving Two, Four, and Six-Legged Locomotion
Chapter 12
Andrea Toffolo
The research field on energy conversion systems presents a large variety of multi-objective optimization problems that can be solved taking full... Sample PDF
Evolutionary Multi-Objective Optimization in Energy Conversion Systems: From Component Detail to System Configuration
Chapter 13
Mark P. Kleeman, Gary B. Lamont
Assignment problems are used throughout many research disciplines. Most assignment problems in the literature have focused on solving a single... Sample PDF
Evolutionary Multi-Objective Optimization for Assignment Problems
Chapter 14
Mark P. Kleeman, Gary B. Lamont
Evolutionary methods are used in many fields to solve multi-objective optimization problems. Military problems are no exception. This chapter looks... Sample PDF
Evolutionary Multi-Objective Optimization in Military Applications
About the Contributors