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Multi-objective optimisation using cellular automata: application to multi-purpose reservoir operation

机译:基于元胞自动机的多目标优化:在多用途油藏作业中的应用

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摘要

In this paper, a weighted cellular automata (CA) is proposed to solve bi-objective reservoir operation optimisation problem considering two objectives of water supply and hydropower production. A mathematically derived updating rule is used contributing to the efficiency of the proposed CA method. The updating rule of the problem is derived by converting the bi-objective problem to a single-objective problem using the well-known weighting method. The proposed method is used to operate the Dez reservoir in Iran over various operation periods of 60, 120, 240 and 480 months to test the performance of the method for operational problems of different scales. Performance of the method is also compared with that of a non-dominated sorting genetic algorithm (NSGAII) as one of the most popular multi-objective evolutionary algorithms. The results indicate that the proposed method is highly efficient compared to the NSGAII while producing comparable results. This is in line with the early findings of superior efficiency and comparable effectiveness of the CA method with the existing evolutionary algorithms for single objective optimisation problems.
机译:针对供水和水电生产两个目标,提出了一种加权元胞自动机(CA)来解决水库双目标优化问题。使用数学推导的更新规则有助于提高提出的CA方法的效率。通过使用众所周知的加权方法将双目标问题转换为单目标问题,可以得出问题的更新规则。所提出的方法用于在伊朗60个月,120个月,240个月和480个月的不同运行周期内运行Dez油藏,以测试该方法对不同规模的运行问题的性能。还将该方法的性能与作为最流行的多目标进化算法之一的非支配排序遗传算法(NSGAII)的性能进行了比较。结果表明,所提出的方法与NSGAII相比是高效的,同时产生了可比的结果。这与CA方法与现有的针对单目标优化问题的进化算法的卓越效率和可比性的早期发现相一致。

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