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Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

机译:基于改进Logistic映射的混沌粒子群优化短期水力发电系统调度

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In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.
机译:为了解决短期级联水力发电系统调度模型,引入了一种改进的逻辑映射的混沌粒子群优化算法,该算法以排水量为决策变量,并结合了死刑函数。根据最大发电原理,提出的方法利用改进的逻辑混沌映射图的遍历性,对称性和随机性来提高粒子群优化算法的性能。新的混合方法已经在两个测试功能和一个实用的级联水电系统上进行了测试。实验结果表明,与其他传统算法相比,该算法具有较高的鲁棒性。

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