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自适应逐次逼近遗传算法及其在水库群长期调度中的应用
Adaptive genetic algorithm successive approximation and its application to long-term reservoirs operation
传统遗传算法的解空间均为可行解, 经过遗传操作产生的新个体若为不可行解, 则需要对其进行修正. 但在梯级水库调度中, 由于各时段间、 水库间存在的水力电力联系, 使这种修正变得复杂困难. 鉴于此, 本文提出了自适应逐次逼近遗传算法(AGASA), 它可在包含不可行解的空间中寻优, 并根据寻优结果自动调整搜索空间与控制参数, 从而逐渐逼近最优解. 最后通过一个算例, 并与离散微分动态规划法(DDDP), 逐步优化法(POA)的优化结果进行比较, 说明了该方法的可行性与有效性.
In traditional genetic algorithm (GA), all chroms in the solution space are feasible; and if the new chroms created by genetic operation become infeasible they need to be revised. In cascade reservoirs operation, however, such revising becomes complicated because of the hydraulic and electric connections between time sequences and between reservoirs. Therefore, an advanced GA——adaptive genetic algorithm successive approximation (AGASA)——is proposed in this paper, which can do optimizing within a space including feasible and infeasible schemes, and finally find the optimum by successively altering the space and adaptively changing control parameters. Finally, a simulated example is provided, and the results are compared to those obtained by discrete differential dynamic programming (DDDP) and progressive optimality algorithm (POA) respectively, which indicates the feasibility and validity of AGASA.
水库调度 / 优化调度 / 遗传算法 / 自适应 / 逐次逼近 {{custom_keyword}} /
reservoir operation / optimal operation / genetic algorithm / adaptive / successive approximation {{custom_keyword}} /
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国家自然科学基金(41061053);云南省自然科学基金(2009ZC005X)
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