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为了研究煤矿节能减排规划期内每年度间的动态多目标关联性,构建了基于自适应控制、差分进化算法与混合聚类算法的智能优化算法,应用自适应控制实时调整变异尺度因子、交叉概率常数的更新策略,得到差分进化算法的pareto解,进而应用混合聚类算法获取投资优化方案,为管理者投资决策提供有效依据。实验结果表明:在较少的迭代步数内,煤炭生产总量、能源消耗量、污染物排放量可以根据目标要求获得协调发展,且提供的投资决策依据切实有效。
In order to study the dynamic multi-objective relevance between coal mines during the annual planning period of energy saving and emission reduction, an intelligent optimization algorithm based on adaptive control, differential evolution algorithm and hybrid clustering algorithm was constructed. Adaptive scaling algorithm was used to adjust real- The updating strategy of the probability constant, the pareto solution of the differential evolution algorithm is obtained, then the hybrid clustering algorithm is used to obtain the investment optimization scheme, which provides an effective basis for the manager’s investment decision. The experimental results show that with a small number of iteration steps, the total coal production, energy consumption and pollutant emissions can be coordinated development according to the target requirements, and the investment decision basis provided is effective.