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