Multi-Colony Ant Algorithm for Continuous Multi-Reservoir Operation Optimization Problem |
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Authors: | M R Jalali A Afshar M A Mariño |
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Affiliation: | (1) Iran University of Science and Technology (IUST) and Mahab Ghodss Consulting Engrs., Tehran, Iran;(2) Department of Civil Engineering and Center of Excellence for Fundamental Studies in Structural Mechanics, Iran University of Science and Technology, Tehran, Iran;(3) Hydrology Program and Department of Civil and Environmental Engineering, University of California, Davis, CA 95616-8628, USA |
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Abstract: | Ant Colony Optimization (ACO) algorithms are basically developed for discrete optimization and hence their application to
continuous optimization problems require the transformation of a continuous search space to a discrete one by discretization
of the continuous decision variables. Thus, the allowable continuous range of decision variables is usually discretized into
a discrete set of allowable values and a search is then conducted over the resulting discrete search space for the optimum
solution. Due to the discretization of the search space on the decision variable, the performance of the ACO algorithms in
continuous problems is poor. In this paper a special version of multi-colony algorithm is proposed which helps to generate
a non-homogeneous and more or less random mesh in entire search space to minimize the possibility of loosing global optimum
domain. The proposed multi-colony algorithm presents a new scheme which is quite different from those used in multi criteria
and multi objective problems and parallelization schemes. The proposed algorithm can efficiently handle the combination of
discrete and continuous decision variables. To investigate the performance of the proposed algorithm, the well-known multimodal,
continuous, nonseparable, nonlinear, and illegal (CNNI) Fletcher–Powell function and complex 10-reservoir problem operation
optimization have been considered. It is concluded that the proposed algorithm provides promising and comparable solutions
with known global optimum results. |
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Keywords: | ant colony optimization multi-colony multi-reservoir |
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