Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method
Engineering and Applied Sciences
Volume 1, Issue 2, August 2016, Pages: 20-25
Received: Jun. 15, 2015; Accepted: Jul. 1, 2015; Published: Jul. 16, 2016
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Hiqmet Kamberaj, Faculty of Engineering, Department of Computer Engineering, International Balkan University, Skopje, R. of Macedonia
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We present a newly developed Replica Exchange algorithm using q -Gaussian Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the problem of finding the global optimum. The basis of the algorithm is to run multiple copies of independent swarms at different values of q parameter. Based on an energy criterion, chosen to satisfy the detailed balance, we are swapping the particle coordinates of neighboring swarms at regular iteration intervals. The swarm replicas with high q values are characterized by high diversity of particles allowing escaping local minima faster, while the low q replicas, characterized by low diversity of particles, are used to sample more efficiently the local basins. We compared the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and found that the new algorithm is more robust in terms of the number of fitness function calls, and more efficient in terms of ability to convergence faster to the global minimum. In additional, we also provide a method for optimally allocating the swarm replicas among different q values. Our algorithm is tested for three benchmark functions, which are known to be multimodal problems, at different dimensionalities.
Swarm Quantum Particle, q-Gaussian Distribution, Global Optimization, Replica Exchange
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Hiqmet Kamberaj, Replica Exchange Using q-Gaussian Swarm Quantum Particle Intelligence Method, Engineering and Applied Sciences. Vol. 1, No. 2, 2016, pp. 20-25. doi: 10.11648/j.eas.20160102.12
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