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为保障梯级水电站群多目标优化调度问题的计算效率和求解精度,提出了基于Fork/Join多核并行框架的并行多目标遗传算法。该方法以多目标遗传算法为基础,引入多种群异步进化策略保证种群间个体多样性;采用迁移机制保障子种群的信息有机互馈,提升算法收敛性和解集多样性;利用并行技术实现子种群在各内核的同步求解,提高计算效率。针对问题特点,耦合个体实数串联编码方法、混沌初始化种群策略和约束Pareto占优机制等,进一步提升方法寻优性能。澜沧江流域梯级水电站群多目标优化调度结果表明,所提方法可充分利用多核资源,提升模型计算效率与求解精度,并能获得分布均匀、合理可行的调度方案集,为水电系统多目标高效决策提供科学依据。
To ensure the computational efficiency and accuracy of multi-objective optimization scheduling problem for cascade hydropower stations, a parallel multi-objective genetic algorithm based on Fork / Join multi-core parallel framework is proposed. Based on multi-objective genetic algorithm, this method introduces multi-population asynchronous evolvement strategy to ensure individual diversity among populations. Migration mechanism is used to ensure the mutual information of information among sub-populations and improve the convergence and solution diversity of the sub-population. Parallel sub-population In the core of the synchronization solution, improve computational efficiency. In view of the characteristics of the problem, coupled real-number series coding method, chaos initialization population strategy and constrained Pareto dominance mechanism, the method optimization performance is further improved. The multi-objective optimal scheduling of cascade hydropower stations in the Lancang River Basin shows that the proposed method can make full use of multi-core resources, improve the computational efficiency and accuracy of the model, and obtain a uniform and feasible scheduling scheme for hydropower system multi-objective and efficient decision-making Scientific basis.