  We present a newly developed -Gaussian Swarm Quantum-like Particle
Optimization (q-GSQPO) algorithm to determine the global minimum of the
potential energy function. Swarm Quantum-like Particle Optimization (SQPO)
algorithms have been derived using different attractive potential fields to
represent swarm particles moving in a quantum environment, where the one which
uses a harmonic oscillator potential as attractive field is considered as an
improved version. In this paper, we propose a new SQPO that uses -Gaussian
probability density function for the attractive potential field (q-GSQPO)
rather than Gaussian one (GSQPO) which corresponds to harmonic potential. The
performance of the q-GSQPO is compared against the GSQPO. The new algorithm
outperforms the GSQPO on most of the time in convergence to the global optimum
by increasing the efficiency of sampling the phase space and avoiding the
premature convergence to local minima. Moreover, the computational efforts were
comparable for both algorithms. We tested the algorithm to determine the lowest
energy configurations of a particle moving in a 2, 5, 10, and 50 dimensional
spaces.
