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提出了一种求解低碳调度下机组组合问题的混沌遗传混合优化方法。采用控制基因与参数基因编码方式对机组发电计划进行编码,通过结合电碳特征优先次序、混沌映射和随机生成3种方法提高初始种群多样性;将混沌迭代搜索引入到遗传算法的进化过程之中,构造新的变异算子,改进遗传算法过早收敛的缺点,并且在变异过程中进行按电碳特征优先权确定的区间偏移,达到了加快算法收敛速度的目的。通过算例验证了混沌遗传混合优化方法具有较好的收敛特性和全局搜索能力。
A chaos genetic hybrid optimization method is proposed to solve the unit commitment problem under low carbon schedule. The generation plan of the generator was coded by coding genes and parameters, and the initial population diversity was improved by combining the charcoal feature prioritization, chaos mapping and random generation. The chaotic iterative search was introduced into the evolutionary process of genetic algorithm , Constructing a new mutation operator, improving the shortcoming of premature convergence of genetic algorithm, and implementing the interval offset determined by the priority of carbon features in the process of mutation, thus achieving the goal of accelerating the convergence rate of the algorithm. An example shows that the chaotic genetic hybrid optimization method has better convergence property and global search ability.