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Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects

机译:基于混合生物地理学的优化与头脑风暴优化,用于具有阀点效应的非凸动态经济调度

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摘要

Dynamic economic dispatch (DED), mathematically, is a typical highly complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering valve-point effects. In this paper, a hybrid method named BBOSB by combining biogeography-based optimization (BBO) with brain storm optimization (BSO) is proposed. BBO has good local exploitation ability due to its information sharing mechanism. But it is likely to suffer from premature convergence when dealing with complex multimodal problems. Quite the opposite, BSO possesses excellent global exploration ability owing to its grouping evolution strategy which, however, also can drag its global searching process. In such contexts, the hybrid BBOSB method is able to fully take advantages of both BBO and BSO to conquer premature convergence and to accelerate the global searching process simultaneously. The experimental and comparison results on four non-convex benchmark DED test cases with valve-point effects and a practical provincial power system of China comprehensively demonstrate that BBOSB is highly competitive and can be used as a promising alternative for DED problems. In addition, the effect of population size on the optimization performance is investigated as well.
机译:从数学上讲,动态经济调度(DED)是一个典型的高度复杂的非线性多变量强耦合优化问题,具有相等和不平等约束,尤其是考虑到阀点效应的情况。本文提出了一种结合了基于生物地理的优化(BBO)和头脑风暴优化(BSO)的混合方法,称为BBOSB。 BBO由于其信息共享机制而具有良好的本地开发能力。但是在处理复杂的多峰问题时,它可能会过早收敛。恰恰相反,BSO由于其分组演化策略而具有出色的全球勘探能力,然而,这也可能拖累其全球搜索过程。在这种情况下,混合BBOSB方法能够充分利用BBO和BSO两者的优势来征服过早的收敛并同时加速全局搜索过程。在四个具有阀点效应的非凸基准DED测试用例和中国实际的省级电力系统上的实验和比较结果全面证明,BBOSB具有很强的竞争力,可以用作解决DED问题的有希望的替代方法。此外,还研究了种群大小对优化性能的影响。

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