International Journal of Civil Engineering
IJCE
Engineering & Technology
http://ijce.iust.ac.ir
18
agent2
1735-0522
2383-3874
10.22068/IJCE
en
jalali
1386
9
1
gregorian
2007
12
1
5
4
online
1
fulltext
en
Multi-reservoir operation by adaptive pheromone re-initiated ant colony optimization algorithm
Research Paper
Research Paper
Through a collection of cooperative agents called ants, the near optimal solution to the
multi-reservoir operation problem may be effectively achieved employing Ant Colony Optimization
Algorithms (ACOAs). The problem is approached by considering a finite operating horizon,
classifying the possible releases from the reservoir(s) into pre-determined intervals, and projecting
the problem on a graph. By defining an optimality criterion, the combination of desirable releases
from the reservoirs or operating policy is determined. To minimize the possibility of premature
convergence to a local optimum, a combination of Pheromone Re-Initiation (PRI) and Partial Path
Replacement (PPR) mechanisms are presented and their effects have been tested in a benchmark,
nonlinear, and multimodal mathematical function. The finalized model is then applied to develop an
optimum operating policy for a single reservoir and a benchmark four-reservoir operation problem.
Integration of these mechanisms improves the final result, as well as initial and final rate of
convergence. In the benchmark Ackley function minimization problem, after 410 iterations, PRI
mechanism improved the final solution by 97 percent and the combination of PRI and PPR
mechanisms reduced final result to global optimum. As expected in the single-reservoir problem,
with a continuous search space, a nonlinear programming (NLP) approach performed better than
ACOAs employing a discretized search space on the decision variable (reservoir release). As the
complexity of the system increases, the definition of an appropriate heuristic function becomes more
and more difficult this may provide wrong initial sight or vision to the ants. By assigning a
minimum weight to the exploitation term in a transition rule, the best result is obtained. In a
benchmark 4-reservoir problem, a very low standard deviation is achieved for 10 different runs and
it is considered as an indication of low diversity of the results. In 2 out of 10 runs, the global optimal
solution is obtained, where in the other 8 runs results are as close as 99.8 percent of the global
solution. Results and execution time compare well with those of well developed genetic algorithms
(GAs).
Ant colony; Optimization; Reservoir operation; Multi-reservoir
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Hon.M.
ASCE
`180031947532846005983`

180031947532846005983
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M.R.
Jalali
`180031947532846005984`

180031947532846005984
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A.
Afshar
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180031947532846005985
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M.A.
MariĆ±o
`180031947532846005986`

180031947532846005986
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