An Improved Harris’s Hawks Optimization for SAR Target Recognition and Stock Market Index Prediction

Abstract

Harris's hawks optimization (HHO) algorithm proposed in 2019 is a novel population-based, nature-inspired optimization paradigm that imitates the cooperative behavior and chasing style of Harris's hawks in nature called surprise pounce. Inspired by particle swarm optimization algorithm, velocity is added into the HHO algorithm in the exploration phase. The soft besiege with progressive rapid dives and the hard besiege with progressive rapid dives in the attacking stages of the HHO algorithm are improved by use of the crossover operator of the artificial tree algorithm. Thus the improved HHO algorithm is obtained, written as IHHO. The effectiveness of the IHHO algorithm is tested on 23 benchmark problems by comparison with the other 11 state-of-art meta-heuristic algorithms. The IHHO algorithm is used to optimize the parameters of support vector machine for synthetic aperture radar (SAR) target recognition and of the extreme learning machine for stock market index prediction by considering Google Trends. The comparable results show that the IHHO algorithm is very promising and has some competitive potential.

Keywords

• Extreme learning machine
• function optimization
• Harris’s hawk optimization
• stock prediction
• support vector machine
• synthetic aperture radar target recognition