Search

In this paper, a novel algorithm “firework optimization” (FO) is introduced, as well as its hybridization with generating set search (GSS). FO is an exploration algorithm, which intends to explore a vast region within the search space in order to determine regions that may contain a regional optimum. As the regions with potential are defined, GSS, the exploitation algorithm is applied to determine whether regions determined by FO contain a local or a global optimum. The validity of FO and GSS hybridization algorithm is verified using Branin and Six-Hump Camel test functions, and the hybridization algorithm is applied to an interior permanent magnet synchronous motor, in order to minimize torque ripple. [ABSTRACT FROM AUTHOR]

In this paper, a novel global search algorithm, implementing principal component optimization (PCO) in combination with mesh adaptive direct search (MADS) is proposed. The PCO is based on principal component analysis, a widely used multivariate technique in statistics, while MADS is a highly effective local search algorithm, implemented to find local optima. Their integration reduces computation time and improves convergence reliability to a global optimum. The proposed algorithm is verified via application of a test function and comparison to conventional hybrid algorithms such as particle swarm optimization with MADS. Finally, the algorithm is applied to the optimal design of an interior permanent magnet synchronous machine for minimization of total harmonic distortion and torque ripple via finite element analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel memetic algorithm, which is explorative particle swarm optimization (ePSO), combined with mesh adaptive direct search and apply it to the design of a permanent magnet synchronous machine (PMSM). The ePSO, which is modified from the PSO, drastically improves search time and iteration number at an exploration search stage. Unlike the existing methods, the proposed rule of start point selection takes an advantage of minimizing the search time. By applying the proposed algorithm to PMSM, we clarify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]