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This paper proposes a modified crow search optimizer (MCSO) for solving the combined economic emission power flow (EEPF) problem. In the proposed approach, the local search ability is enhanced into the crow search optimizer (CSO) and aggregated with a novel bat algorithm (NBA). Close accord between CSO, NBA, and MCSO is employed for solving the single and multi-objective frameworks. Moreover, the proposed MCSO incorporates external archive and dominance comparison to handle multi-objective frameworks while the best compromise solution is extracted by using a fuzzy based mechanism. The proposed MCSO, CSO, and NBA are developed and tested to on IEEE 30 bus and West Delta power grid (WDPG) systems. Added to the that, the proposed methodology is tested on a large-scale power system, IEEE 118-bus test system, for measure the scalability of the proposed method. Their output results are compared with the reported algorithms in the literature to demonstrate the MCSO outperformance in terms of solution quality and robustness. Significant economical solutions of the EEPF problem are achieved with respecting the environment concerns at acceptable emission levels. Added to that, the multi objective framework is assessed with hypervolume indictor that show the high capability of the proposed MCSO compared with CSO.

A Modified Crow Search Optimizer for Solving Non-Linear OPF Problem With Emissions