A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO’s strong global search capability and LS’s fast convergence ability. This work proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art PSO variants.

This paper evaluates the performance of neutronic and thermal-hydraulic coupling algorithms in transient problems based on the high-temperature gas-cooled reactor simulator TINTE. In particular, the operator splitting semi-implicit (OSSI), Picard iteration, and Jacobian-free Newton-Krylov (JFNK) methods are compared by a practical engineering model. The OSSI method is employed in the original TINTE. The fully implicit algorithms TINTE-Picard and TINTE-JFNK are implemented in this study. Several special numerical technologies are discussed to improve the performance of JFNK. First, a novel JFNK variant is employed to deal with the multiscale coupling between local fuel sphere temperature and global solid porous media temperature. Second, the preconditioning strategy is determined by making a balance between performance and code burden. Finally, the scaling modifications of the Jacobian matrix and perturbation size are investigated to solve the ill-posed problem. What is more, the framework of TINTE-Picard and TINTE-JFNK is presented, and the key points of implementation are discussed. Numerical results indicate that the advanced coupling algorithms Picard and JFNK can achieve higher computational performance than the original semi-implicit coupling algorithm in TINTE due to the accuracy and stability advantage.