NDLI: Particle Swarm Optimization: Application to Reservoir Operation Problems

Content Provider	IEEE Xplore Digital Library
Author	Moradi, A.M. Dariane, A.B.
Copyright Year	2009
Description	Author affiliation: Associate Professor, Civil Engineering Dept., Khaje Nasir Toosi University of Technology, Tehran, Iran. dariania@yahoo.com or borhani@kntu.ac.ir (Dariane, A.B.) \|\| Graduate Student, Civil Engineering Dept., Khaje Nasir Toosi University of Technology, Tehran, Iran. amirmorady@gmail.com or amirmoradi@sina.kntu.ac.ir (Moradi, A.M.)
Abstract	This research is intended to evaluate the Particle Swarm Optimization (PSO) algorithms for solving complex problems of water resources management. To achieve the goal, the standard particle swarm optimization algorithm and the modified method named Elitist-Mutation Particle Swarm Optimization (EMPSO) are used to determine optimal operating of a single reservoir system with 504 decision variables. The two methods were compared and contrasted with other meta-heuristic methods such as Genetic Algorithm (GA), and original and modified Ant Colony Optimization in continuous domains (ACOR). The results indicated that the use of EMPSO in complex problems is remarkably superior to the PSO in terms of run time and the optimal value of objective function. Moreover, EMPSO was found comparable to other above stated meta-heuristic methods.
Starting Page	1048
Ending Page	1051
File Size	3359069
Page Count	4
File Format	PDF
ISBN	9781424429271
DOI	10.1109/IADCC.2009.4809159
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Optimization methods Birds Rivers particle swarm optimization Particle swarm optimization Genetic algorithms reservoir operation genetic algorithm ant colony optimization Reservoirs Dynamic programming meta-heuristics Water resources Civil engineering
Content Type	Text
Resource Type	Article

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