GPU-Based CAPSO with N-Dimension Particles

Abstract

Today we are living in a world that is surrounded with information obesity which is also known as Big Data. Big data deals with zeta bytes of data flown from variety sources, and cannot be processed or analyzed using traditional procedure. Due to this, there is an increasing interest of researchers in using low cost GPUs for various applications that require intensive parallel computing to solve complex problems much faster. Various machine learning algorithms have been developed to obtain the optimal solutions with various data complexity. However, for big data problems, new machine learning algorithms need to be developed to deal with zeta bytes data problems. Centripetal accelerated particle swarm optimization (CAPSO) is the recent machine learning algorithm to enhance the convergence speed, accuracy and global optimality for optimization problems. However, the convergence speed of CAPSO is limited for small number of particles only. Hence, this research proposes improved CAPSO by implementing this algorithm on GPU platform through CUDA programming to handle N-dimensional scale of particles. Since CAPSO is intrinsically parallel processing, thus it can be effectively implemented on Graphics Processing Units (GPUs) according. The proposed GPU-based CAPSO was tested on various multi modal test functions and the results have proven that the proposed GPU-based CAPSO has successfully reduced the execution time with various particles dimensions compared to CPU-based CAPSO.