2013/11/17 by Hiqmet Kamberaj, Kamberaj, Hiqmet
Computer Science · Mathematics · Social Sciences · #Algorithm #Artificial Intelligence (cs.AI) #Benchmark (surveying) #Computer science #Convergence (economics) #Diverse Research Studies Overview #FOS: Computer and information sciences #Function (biology) #Gaussian #Mathematical analysis #Mathematical optimization #Mathematics #Maxima and minima #Multi-swarm optimization #Particle swarm optimization #Physics #Quantum #Quantum mechanics #Replica #Swarm behaviour #Swarm intelligence #cs.AI
paper · pdf · doi:10.48550/arxiv.1312.7326
published in arXiv (Cornell University) (Cornell University) · 10 pages, 5 figures, 1 table
arxiv created 2013/11/17 · openalex publication_date 2013/11/17 · arxiv updated 2013/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 compare the new algorithm with the standard Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm Quantum Particle Optimization (q-GSQPO) algorithms, and we found that the new algorithm is more robust in terms of the number of fitness function calls, and more efficient in terms ability convergence to the global minimum. In additional, we also provide a method of 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. In addition, we considered a polyalanine peptide of 12 residues modeled using a Gō coarse-graining potential energy function.