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We present a newly developed -Gaussian Swarm Quantum-like ParticleOptimization (q-GSQPO) algorithm to determine the global minimum of thepotential energy function. Swarm Quantum-like Particle Optimization (SQPO)algorithms have been derived using different attractive potential fields torepresent swarm particles moving in a quantum environment, where the one whichuses a harmonic oscillator potential as attractive field is considered as animproved version. In this paper, we propose a new SQPO that uses -Gaussianprobability density function for the attractive potential field (q-GSQPO)rather than Gaussian one (GSQPO) which corresponds to harmonic potential. Theperformance of the q-GSQPO is compared against the GSQPO. The new algorithmoutperforms the GSQPO on most of the time in convergence to the global optimumby increasing the efficiency of sampling the phase space and avoiding thepremature convergence to local minima. Moreover, the computational efforts werecomparable for both algorithms. We tested the algorithm to determine the lowestenergy configurations of a particle moving in a 2, 5, 10, and 50 dimensionalspaces.

Q-Gaussian Swarm Quantum Particle Intelligence on Predicting Global Minimum of Potential Energy Function