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2009 |
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Transaction on Civil Engineering |
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Transaction on Mechanical Engineering |
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Transactions on Chemistry and Chemical Engineering |
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Transaction on Computer Science & Engineering and Electrical Engineering |
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Transaction on Industrial Engineering |
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Transaction on Nanotechnology |
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Volume 16, Issue 4, 2009
Transaction on Civil Engineering
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Elitist-Mutated Ant System Versus
Max-Min Ant System: Application to
Pipe Network Optimization Problems
M.H. Afshar (PhD.)
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Abstract: The Ant Colony Optimization Algorithm (ACOA) is a new class of stochastic search
algorithm proposed for the solution of combinatorial optimisation problems. Dierent versions of ACOA
are developed and used with varying degrees of success. The Max-Min Ant System (MMAS) is recently
proposed as a remedy for the premature convergence problem often encountered with ACOAs using elitist
strategies. The basic concept behind MMAS is to provide a logical balance between exploitation and
exploration. The method, however, introduces some additional parameters to the original algorithm,
which should be tuned for the best performance of the method adding to the computational requirement
of the algorithm. An alternative method to MMAS is proposed in this paper and applied to pipe network
optimization problem. The method uses a simple but eective mechanism, namely Pheromone Trail
Replacement (PTR), to make sure that the global best solution path has always the maximum trail
intensity. This mechanism introduces enough exploitation into the method and more importantly enables
one to exactly predict the number of global best solutions at each iteration of the algorithm without requiring
calculation of the cost of the solutions created. The sub-colony of repeated global best solutions of the
iterations is then mutated, such that a predened number of solutions survive the mutation process. Two
dierent mutation mechanisms, namely deterministic and stochastic mutation processes, are introduced
and used. The rst one uses a one bit mutation with a probability of one on some members of the
sub-colony, while the second one uses a uniform mutation on the whole sub-colony. The probability of
mutation in the second mutation process is adjusted at each iteration, so that the required number of globalbest
solutions survives the mutation. The method is shown to produce results comparable to the MMAS
algorithm, while requiring less free parameter tuning. The application of the method to a benchmark
example in the pipe network optimization discipline is presented and the results are compared.
Keywords: MutatedAnt colony optimization algorithm |
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Application of an Ant Colony Optimization
Algorithm for Optimal Operation of
Reservoirs: A Comparative Study
of Three Proposed Formulations
M.H. Afshar (PhD.)
R. Moeini [MSc.]
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Abstract: This paper presents an application of the Max-Min Ant System for optimal operation of
reservoirs using three dierent formulations. Ant colony optimization algorithms are a meta-heuristic
approach initially inspired by the observation that ants can nd the shortest path between food sources and
their nest. The basic algorithm of Ant Colony Optimization is the Ant System. Many other algorithms,
such as the Max-Min Ant System, have been introduced to improve the performance of the Ant System.
The rst step for solving problems using ant algorithms is to dene the graph of the problem under
consideration. The problem graph is related to the decision variables of problems. In this paper, the
problem of optimal operation of reservoirs is formulated using two dierent sets of decision variable, i.e.
storage volumes and releases. It is also shown that the problem can be formulated in two dierent graph
forms when the reservoir storages are taken as the decision variables, while only one graph representation
is available when the releases are taken as the decision variables. The advantages and disadvantages of
these formulation are discussed when an ant algorithm, such as the Max-Min Ant System, is attempted
to solve the underlying problem. The proposed formulations are then used to solve the problem of water
supply and the hydropower operation of the \Dez" reservoir. The results are then compared with each
other and those of other methods such as the Ant Colony System, Genetic Algorithms, Honey Bee Mating
Optimization and the results obtained by Lingo software. The results indicate the ability of the proposed
formulation and, in particular, the third formulation to optimally solve reservoir operation problems.
Keywords: Ant colony optimizationMax-Min Ant SystemGraphOptimal operation of reservoir;
Hydropower reservoir. |
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Improving Penalty Functions
for Structural Optimization
A. Joghataie (PhD.)
M. Takalloozadeh [MSc.]
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Abstract: New penalty functions, which have better convergence properties, as compared to the
commonly used exterior and interior penalty functions, have been proposed in this paper. The convergence
behavior and accuracy of ordinary penalty functions depend on the selection of appropriate penalty
parameters. The optimization of ordinary penalty functions is accomplished after several rounds of
optimization where, at each round a dierent but xed value of penalty parameter is used. While some
useful hints and rules for the selection of suitable penalty parameter values have been provided by dierent
authors, the objective of this paper has been to improve this procedure by including the penalty parameter
in the design vector, so that it can be modied during the optimization, automatically, in order to improve
the convergence characteristics. This can also help accomplish optimization in only one round, which is
of considerable importance when it is desired to solve a constrained problem by using genetic algorithms.
The proof of convergence to the optimum solution of the proposed functions is also included in the paper.
Ten-bar and three-bar truss examples are used for illustration through which the convergence of ordinary
and new functions are evaluated and compared. The results show that the new penalty functions can
outperform the ordinary functions, especially in combination with genetic algorithms.
Keywords: Optimization |
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Ductility of High Strength Concrete
Heavily Steel Reinforced Members
A.A. Maghsoudi (PhD.)
Y. Sharifi [MSc.]
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Abstract: The nature of High Strength Concrete, HSC, is brittle failure and although the behavior of
reinforced concrete beams heavily steel reinforced are increased in strength, the ductility, which is important
in seismic regions, is in question. In other words, such beams, while consisting of HSC, are more brittle.
In this paper, the
exural ductility of such members, with a variation in compressive reinforcement, is
investigated. Six heavily reinforced High Strength Concrete, HSC, beams, with dierent percentages of
and 0, were cast and incrementally loaded under bending. During the test, the strain on the concrete
middle face and on the tension and compression bars as well as the de
ection at dierent points of the
span length were measured up to failure. Based on the results obtained, the curvature, displacement and
rotation ductility of the HSC members are more deeply reviewed. A comparison between theoretical and
experimental results are also reported here. Generally, it was concluded that for heavily steel reinforced
HSC beams, the displacement ductility for singly reinforced beams is too close to the doubly reinforced
beams.
Keywords: HSC |
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