A comparative study of marriage in honey bees optimisation (MBO) algorithm in multi-reservoir system optimisation

Contemporary reservoir systems often require operators to meet a variety of goals and objectives; these in turn frequently complicate water management decision-making. In addition, many reservoir objectives have non-linear relationships and are therefore difficult to implement using traditional opti...

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Bibliographic Details
Published in:Water S. A. 2013-04, Vol.39 (2), p.327-334
Main Authors: Dariane, Alireza B., Farahmandfar, Zeinab
Format: Article
Language:English
Subjects:
Algorithms
Animal reproduction
Ant colony optimization
Apis mellifera
Bees
Biological and medical sciences
Comparative studies
Earth sciences
Earth, ocean, space
Entomology
Evolutionary algorithms
Exact sciences and technology
Formicidae
Fundamental and applied biological sciences. Psychology
Genetic algorithms
Honey
Honey bee mating optimisation algorithm
Hydrology
Hydrology. Hydrogeology
Management
Mathematical optimization
Meta-heuristic algorithms
Multi reservoir system
Non-linear optimisation
Optimization
Optimization algorithms
Reservoirs
Supplying
Water
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Summary:Contemporary reservoir systems often require operators to meet a variety of goals and objectives; these in turn frequently complicate water management decision-making. In addition, many reservoir objectives have non-linear relationships and are therefore difficult to implement using traditional optimisation techniques. A practical application of the marriage in honey bees optimisation (MBO) algorithm is being utilised for Karkheh multi-reservoir system, south-western Iran, where supplying irrigation water for agricultural areas and maintaining a minimum in-stream flow for environmental purposes is desired. Optimal monthly reservoir release information by MBO is highlighted and the results compared to those of other evolutionary algorithms, such as the genetic algorithm (GA), ant colony optimisation for continuous domains (ACOR), particle swarm optimisation (PSO) and elitist-mutation particle swarm optimisation (EMPSO). The results indicate the superiority of MBO over the algorithms tested.
ISSN:0378-4738
0378-4738
DOI:10.4314/wsa.v39i2.17