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Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

机译：改进的具有多样性信息的量子启发式进化算法应用于禁运区的经济调度问题

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The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.
机译：具有复杂性和高度非线性特征的发电经济调度问题（EDP）的目标是调度承诺的发电机组输出，以便以最小的运行成本满足所需的负载需求，同时满足所有机组和系统的平等要求。和不平等约束。最近，作为传统数学方法的替代方法，现代元启发式优化技术由于能够在EDP中找到几乎全局的最优解而备受关注。自1990年代末以来，已经开始进行融合进化计算和量子计算的研究。在量子计算的启发下，本文提出了一种基于种群多样性信息的改进的量子启发式进化算法（IQEA）。实施了经典的量子启发式进化算法（QEA）和IQEA，并验证了EDP的基准，该基准具有15台带有禁止运行区域的热发生器。从基准问题的结果可以看出，与文献中提供的各种方法相比，建议的IQEA方法可提供令人鼓舞的结果。

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