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This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives. [ABSTRACT FROM AUTHOR]

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator's strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation. [ABSTRACT FROM AUTHOR]