Abstract
This paper suggests a new version of Jellyfish Optimizer (JFO) integrated with Quasi-Reflection (QR) in solving the Optimal Power Flow (OPF) problem, considering fuel costs, emissions and losses. Despite the high simplicity of the basic framework of the JFO with substantial features in intensifying and exploring the search space, it needs further assistance in improving its searching ability. The suggested QRJFO creates a uniformly chosen cluster inside the jellyfish population. It represents a shared network that exchanges knowledge in a cluster which is distinct from everyone else. Furthermore, the discovery process is facilitated by the advent of the learning strategy of QR points. The efficiency of the suggested methodology is measured by implementing it on IEEE 30 bus-system. The simulation outputs display the solution effectiveness and the applicability of the suggested QRJFO relative to JFO as well as other documented implementations.