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.

Highlights

  • Along with the significant increase in the processing power of computer hardware and software, a large number of excellent meta-heuristics were created in the intelligent computing field [1,2,3,4,5]

  • The exploration phase of the basic Harris Hawks Optimization (HHO) algorithm uses Equations (1)–(3), where the optimal solution they rush towards their prey at a faster speed until the distance from the prey is small

  • In order to select the best effective chaotic mapping method among seven well-known chaotic mapping methods, which enables us to obtain the best initial solution position and speed up the convergence of the Harris Hawk algorithm population, sinusoidal chaotic mapping, Tent chaotic mapping, Kent chaotic mapping, Cubic chaotic mapping, Logistic chaotic mapping, Gauss chaotic mapping, Circle chaotic mapping were initialized to the population of the HHO algorithm, respectively, forming Sinusoidal-HHO, Tent-HHO, Kent-HHO, Cubic-HHO, Logistic-HHO, Gauss-HHO, Circle-HHO, and we compared the accuracy of these seven algorithms

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Summary

Introduction

Along with the significant increase in the processing power of computer hardware and software, a large number of excellent meta-heuristics were created in the intelligent computing field [1,2,3,4,5]. The original HHO method could not fully balance the exploration and exploitation phases, which resulted in insufficient global search capability and slow convergence of the HHO method. To alleviate these adverse effects, we propose an improved algorithm model called chaotic multi-strategy search. HHO (CSHHO), which introduces chaotic mapping and global search strategy, to solve single-objective optimization problems efficiently. The HHO algorithm based on Gauss chaotic mapping with multi-strategy search is tested It is compared with other classic and state-of-the-art algorithms on 23 classic test functions and 30 IEEE CEC2017 competition functions to verify the significant superiority of the proposed paradigm over other algorithms by Friedman test and Bonferroni–Holm corrected Wilcoxon signed-rank test.

Harris Hawks Optimization Algorithm
Reasons for Improving the Basic HHO Algorithm
Chaotic Mapping
Adaptive Weight
Variable Spiral Position Update
Optimal Neighborhood Disturbance
Computational Complexity
Algorithm Procedure
Benchmark Functions Verification
Efficiency Analysis of the Improvement Strategy
Influence of Seven Common Chaotic Mappings on HHO Algorithm
Comparison with Conventional Techniques
Comparison with HHO Variants
Scalability Test on CSHHO
Engineering Application
Principle of LSSVM
Simulation and Verification
13. The showsshows the absolute deviation range of model from
Findings
Conclusions
Full Text
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