TY - GEN
T1 - Multi-objective mobile agent-based sensor network routing using MOEA/D
AU - Konstantinidis, Andreas
AU - Charalambous, Christoforos
AU - Zhou, Aimin
AU - Zhang, Qingfu
PY - 2010
Y1 - 2010
N2 - Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing "good" tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a Multiobjective Optimization Problem (MOP) using conventional Multi-Objective Evolutionary Algorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.
AB - Mobile agents are often used in wireless sensor networks for distributed target detection with the goal of minimizing the transmission of non-critical data that negatively affects the performance of the network. A challenge is to find optimal mobile agent routes for minimizing the data path loss and the sensors energy consumption as well as maximizing the data accuracy. Existing approaches deal with the objectives individually, or by optimizing one and constraining the others or by combining them into a single objective. This often results in missing "good" tradeoff solutions. Only few approaches have tackled the Mobile Agent-based Distributed Sensor Network Routing problem as a Multiobjective Optimization Problem (MOP) using conventional Multi-Objective Evolutionary Algorithms (MOEAs). It is well known that the incorporation of problem specific knowledge in MOEAs is a difficult task. In this paper, we propose a problem-specific MOEA based on Decomposition (MOEA/D) for optimizing the three objectives. Experimental studies have shown that the proposed problem-specific approach performs better than two conventional MOEAs in several WSN test instances.
UR - https://www.scopus.com/pages/publications/79959403668
U2 - 10.1109/CEC.2010.5586431
DO - 10.1109/CEC.2010.5586431
M3 - 会议稿件
AN - SCOPUS:79959403668
SN - 9781424469109
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Y2 - 18 July 2010 through 23 July 2010
ER -