Paper Info
Title
Evolutionary techniques versus swarm intelligences: application in reservoir release optimization
Abstract
In this paper, a nonlinear reservoir release optimization problem has been solved by using four optimization tools with various combinations of input parameters that are generally used in this research field. A comparison has been made between evolutionary methods [genetic algorithm (GA)] and swarm intelligences [particle swarm optimization (PSO) and artificial bee colony (ABC) optimization] in searching the optimum reservoir release policy. From the historical recorded data, the monthly inflow was categorized into three states: high, medium and low. As a guideline for the decision maker, an optimum release curve was generated for each month showing the release options with a variety of different storage conditions. GA (real and binary), ABC optimization and PSO algorithm have been used as optimization tools with the same formulation and objective function for all the methods. For verification of the models, a simulation is done by using 264 monthly historical inflow data. Different indices such as reliability, vulnerability and resiliency were calculated in order to check the performance and risk analysis purposes. The results show that the most recently developed ABC optimization technique provides the best results in meeting demands, avoiding wastage of water and in handling critical period of low flows.
Year
DOI
Venue
2014
10.1007/s00521-013-1389-8
Neural Computing and Applications
Keywords
DocType
Volume
artificial bee colony optimization,genetic algorithms,optimal reservoir release policy,particle swarm optimization
Journal
24
Issue
ISSN
Citations 
7-8
1433-3058
7
PageRank 
References 
Authors
0.58
9
2
Name
Order
Citations
PageRank
Md S. Hossain1131.82
Ahmed El-Shafie224725.83