This paper proposes a novel hybrid multi-objective algorithm named Multi-objective Spotted Hyena and Emperor Penguin Optimizer (MOSHEPO) for solving both convex and non-convex economic dispatch and micro grid power dispatch problems. The proposed algorithm combines two newly developed bio-inspired optimization algorithms namely Multi-objective Spotted Hyena Optimizer (MOSHO) and Emperor Penguin Optimizer (EPO). MOSHEPO contemplates many non-linear characteristics of power generators such as transmission losses, multiple fuels, valve-point loading, and prohibited operating zones along with their operational constraints, for practical operation. To evaluate the effectiveness of MOSHEPO, the proposed algorithm has been tested on various benchmark test systems and its performance is compared with other well-known approaches. The experimental results demonstrate that the proposed algorithm outperforms other algorithms with low computational efforts while solving economic and micro grid power dispatch problems.

中文翻译：

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MOSHEPO：解决经济负荷分配和微电网问题的混合多目标方法

提出了一种新颖的混合多目标算法，称为多目标斑点鬣狗和帝企鹅优化器（MOSHEPO），用于解决凸和非凸经济调度和微电网功率调度问题。该算法结合了两种新近开发的生物启发式优化算法，即多目标斑点鬣狗优化器（MOSHO）和帝企鹅优化器（EPO）。MOSHEPO考虑了发电机的许多非线性特性，例如传输损耗，多种燃料，阀点负载和禁止的操作区域，以及它们的操作限制，以进行实际操作。为了评估MOSHEPO的有效性，该算法已在各种基准测试系统上进行了测试，并将其性能与其他知名方法进行了比较。