13 research outputs found
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Efficiency of evolutionary algorithms in water network pipe sizing
The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems
Optimal short-term reservoir operation with integrated long-term goals
A methodology to incorporate long-term goals within the short-term reservoir operation optimization model is proposed. Two conflicting objectives for the management of hydropower generation in two different power plants are incorporated. A chance-constrained optimization model is used to derive long-term (annual) operation strategies. With the time horizon of operation for the short-term optimization model kept equal to a single time-step of the long-term optimization model, the optimum end storages derived from the long-term model are incorporated as constraints (storage lower bounds) within the short-term model. The long-term benefits accrued from such an operation model are illustrated for a small reservoir, in South India. The solutions are compared with the historic operation. These are also compared with the solutions of a short-term optimal operation model ignoring long-term goals. The optimization model is solved using a multi-objective genetic algorithm