Parallel particle swarm optimization algorithm based on spatial equal-scale segmentation and hybrid strategy

According to the characteristics of classical PSO algorithm(s), this paper uses the spatial equal-scale segmentation method (PESS) to effectively reduce the data dimension because of the low efficiency of PSO algorithm caused by big data and large population size. Diversity selection and global optimization, according to the law of evolution selection, the local optimization is focused on the two extremes of minimum value and maximal value, thus reducing the number of calculations of the fitness function of the algorithm. On this basis, we introduce a time-hop and elite Gaussian mixture strategy that can overcome the particle plunging into local optimum, while improving the efficiency of the algorithm, the generalization ability of the algorithm is taken into account. In order to prove the performance of FSPSO, we chose PSO and the self-organizing migration algorithm (SOMA) evolved from PSO that were tested in 30, 60 and 100 comparison tests on 5 different test function set with different degrees of complexity. The experimental results show that we proposed FSPSO algorithm has good effectiveness, robustness, complexity and universality in solving global optimization problems.