Optimization of Multi-Objective Coverage Strategy Based on Multiple Particle Swarm Coevolutionary Algorithm for Water Environment Monitoring System

This paper built a multi-objective optimization model and proposed an improved multi-objective particle swarm optimization algorithm called MPS2O ,which is based on Multiple Particle Swarm Co-evolutionary. The MPS2O algorithm has considerable potential for solving multi-objective optimization proble...

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Bibliographic Details
Published in:Applied Mechanics and Materials Vol. 742; no. Sensors, Mechatronics and Automation II; pp. 360 - 363
Main Authors: Tian, Li Wei, Zhao, Hong Wei
Format: Journal Article
Language:English
Published: Zurich Trans Tech Publications Ltd 01.03.2015
Subjects:
Algorithms
Environmental monitoring
Mathematical analysis
Mathematical models
Optimization
Remote sensors
Strategy
Swarm intelligence
Water Environment Monitoring
Co-Evolution
Particle Swarm
Swarm Intelligence
ISBN:3038354236, 9783038354239
ISSN:1660-9336, 1662-7482, 1662-7482
Online Access:Get full text
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Summary:This paper built a multi-objective optimization model and proposed an improved multi-objective particle swarm optimization algorithm called MPS2O ,which is based on Multiple Particle Swarm Co-evolutionary. The MPS2O algorithm has considerable potential for solving multi-objective optimization problems. Mathematical benchmark functions also shows that the proposed algorithm is an excellent Alternative for solving multi-objective optimization problems. Making full use of the research findings home and abroad, MPS2O has been chosen to be the coverage optimization strategy of the wireless sensor networks in Water Environment Monitoring System. Simulation results demonstrate that the MPS2O algorithm is more efficient than the PSO algorithm in solving this real-world problem.
Bibliography:Selected, peer reviewed papers from the 2014 2nd International Conference on Sensors, Mechatronics and Automation (ICSMA 2014), December 28-29, 2014 Shenzhen, China
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ISBN:3038354236
9783038354239
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.742.360