Applying a real-coded multi-population genetic algorithm to multi-reservoir operation

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Abstract

The primary objective of this study is to propose a real-coded hypercubic distributed genetic algorithm (HDGA) for optimizing reservoir operation system. A conventional genetic algorithm (GA) is often trapped into local optimums during the optimization procedure. To prevent premature convergence and to obtain near-global optimal solutions, the HDGA is designed to have various subpopulations that are processed using separate and parallel GAs. The hypercubic topology with a small diameter spreads good solutions rapidly throughout all of the subpopulations, and a migration mechanism, which exchanges chromosomes among the subpopulations, exchanges information during the joint optimization to maintain diversity and thus avoid a systematic premature convergence toward a single local optimum. Three genetic operators, i.e. linear ranking selection, blend-α crossover and Gaussian mutation, are applied to search for the optimal reservoir releases. First, a benchmark problem, the four-reservoir operation system, is considered to investigate the applicability and effectiveness of the proposed approach. The results show that the known global optimal solution can be effectively and stably achieved by the HDGA. The HDGA is then applied in the planning of a multi-reservoir system in northern Taiwan, considering a water reservoir development scenario to the year 2021. The results searched by an HDGA minimize the water deficit of this reservoir system and provide much better performance than the conventional GA in terms of obtaining lower values of the objective function and avoiding local optimal solutions.

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