Harris Hawks Optimization with Multi-Strategy Search and Application

Abstract

The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability.

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

“…Jiao et al [162] proposed a multistrategy search HHO using the Least Squares Support Vector Machine (LSSVM). They used the Gauss chaotic method as the initialization method.…”

YOLOv8 with Multi Strategy Integrated Optimization and Application in Object Detection

“…With the continuous research on deep learning, Alexey Bochkovskiy et al proposed a new YOLOv4 model in 2020 [10] , which used CSPDarknet53 backbone network and Mosaic data enhancement method to modify the parameters of the model through self-adversarial training to improve the stability and accuracy of the model, and was trained on MS COCO dataset, achieving 43.5% AP, but due to the complexity of the model and the large amount of computation, which resulted in a long training time, and when dealing with small-scale dataset is prone to overfitting problem. Afterwards, some teams also further improved YOLOv4 by replacing the CSPBlock module with the ResBlock-D module [11] that aims to increase the training speed, reduce the complexity, and apply to real-time detection, but again it is difficult to balance the contradiction between model size and detection accuracy.…”

A Novel Brownian Motion-based Hybrid Whale Optimization Algorithm

“…It can expand the predation area and increase the randomness of the algorithm [20]. To obtain a better search effect, Shangbin Jiao et al [21] added a nonlinear weight ( ) w t  in the HHO search process, which improved the early global search ability. So, the Harris hawk search strategy with nonlinear weights is introduced into the WOA search-predation stage, and its expression is as follows [21]:…”