A Novel Human Learning Optimization Algorithm Incorporating PSO

Aiming at the issues of slow astringent velocity and low optimization precision of Human Learning Optimization Algorithm (HLO), it is proposed to fuse the improved HLO algorithm with Particle Swarm Optimization (PSO) to form Industrious Humans Learn Optimization Algorithms (IHLO). Firstly,the search performance of HLO is improved by using the particle update method in PSO for the update of the three operators of HLO; secondly, the probability of the parameters pr and pi is improved by adjusting their value changes to control the execution probability of each operator using nonlinear decreasing and nonlinear increasing respectively, which good improvement in algorithmic optimization seeking. Finally, the new optimization algorithm IHLO is compared with the HLO and the HLO-PSO . The experimental results show that IHLO significantly outperforms the HLO algorithm and HLO-PSO algorithm in both the speed of convergence and search precision.