Amplified and quantum based brain storm optimization algorithms for real power loss reduction

Abstract

In this work Amplified Brain Storm Optimization (ABS) algorithm and Quantum based Brain Storm (QBS) Optimization Algorithm is used for solving optimal reactive power problem. In the projected amplified Brain storm optimization algorithm Hamiltonian cycle has been applied to improve the search abilities and also to avoid of trap in local optimal solution. A node is arbitrarily chosen from the graph as the preliminary point to form a Hamiltonian cycle. At generation t and t+1, L<sub>t</sub> and L<sub>t</sub><sub>+1</sub> are the length of Hamiltonian cycle correspondingly. In the QBS algorithm a Quantum state of an idea is illustrated by a wave function as an alternative of the position modernized only in Brain storm optimization algorithm. Monte Carlo simulation method<em> </em>is used, to measure the position for each idea from the<em> </em>quantum state to the traditional one. Proposed Amplified Brain Storm Optimization (ABS) algorithm and Quantum based Brain Storm (QBS) Optimization Algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithms reduced the real power loss effectively.