A Novel Quasi-Oppositional Jaya Algorithm for Optimal Power Flow Solution

This article introduces a new meta-heuristic algorithm, namely quasi-oppositional Jaya (QOJaya) algorithm for solving the optimal power flow (OPF) problem. In this approach, an intelligence strategy, namely quasi-oppositional based learning (QOBL) is integrated into the original Jaya algorithm to enhance its convergence rapidity and solution optimality. The suggested QOJaya algorithm to deal with single objective OPF problem is scrutinized and validated using the IEEE 30-bus test network. The obtained results reveal the supremacy of the proposed QOJaya algorithm over the basic Jaya algorithm in terms of solution quality and execution time. In addition, the results show the superiority of the proposed QOJaya algorithm over many existing heuristics optimization algorithms introduced in the literature in terms of solution feasibility and optimality.

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