Harris Hawks Optimization: A Formal Analysis of Its Variants and

Applications

Ruba Abu Khurma

, Ibrahim Aljarah

and Pedro A. Castillo

King Abdullah II School for Information Technology,

The University of Jordan, Amman, Jordan

Department of Computer Architecture and Computer Technology, ETSIIT and CITIC,

University of Granada, Granada, Spain

Keywords:

Harris Hawcks Optimization Algorithm, HHO, Meta-heuristic Optimization, Swarm Intelligence Algorithms.

Abstract:

The Harris Hawks Optimization (HHO) is a recent meta-heuristic algorithm developed by Hideri in 2019.

HHO algorithm has been widely utilized to solve many optimization problems in different ﬁelds. The primary

objective of HHO is to deﬁne a ﬁtness function that can successfully optimize a speciﬁc problem by ﬁnding the

minimum or maximum value. This survey presents a thorough study of the algorithm, including its variants

such as binary, hybridization, multi-objective and modiﬁcations. It highlights the main applications such as

medical, engineering, machine learning, and network applications. Finally, the conclusion summarizes the

current works on HHO and suggests possible future directions.

1 INTRODUCTION

Harris Hawks Optimization (HHO) is a new swarm-

based algorithm developed by Heidari in 2019 (Hei-

dari et al., 2019). HHO mimics the hunting strategy

of Harris hawks in nature. HHO has two phases of

exploration and four phases of exploitation.

The main motivation of this survey is to provide

a full review of the HHO algorithm including its us-

age to address different optimization problems in dif-

ferent applications. Moreover, this survey focuses

on the main modiﬁcations applied in the literature to

enhance the performance of the HHO algorithm and

to tackle its shortcomings. Besides, the survey uses

references from various well-known publishers (e.g,

IEEE, Springer, Elsevier, Hindawi, Taylor & Fran-

cis, and other publishers). Fig 1 shows the number

of published papers that are classiﬁed based on the

publisher of the HHO algorithm publications. Fig 2

shows the classiﬁcation of these papers based on the

applications.

This survey presents and discusses the HHO algo-

rithm based on two major considerations:

• Theoretical aspects of HHO algorithm including

the HHO modiﬁcations, hybridization, and multi-

objective.

• Applications of HHO algorithm including envi-

ronment, manufacturing, energy, power system,

Figure 1: Number of publications of HHO algorithm per

publisher.

Figure 2: Number of publications of HHO algorithm per

applications.

features selection, medical applications, network

applications, engineering, image processing, and

other applications.

The rest of this survey is organized as follows: in Sect.

2, the theoretical aspects of the HHO algorithm and its

Khurma, R., Aljarah, I. and Castillo, P.

Harris Hawks Optimization: A Formal Analysis of Its Variants and Applications.

DOI: 10.5220/0010636600003063

In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021), pages 88-95

ISBN: 978-989-758-534-0; ISSN: 2184-2825

variants are summarized. In Sect. 3, applications of

HHO algorithm is outlined and highlighted. Assess-

ment and evaluation of HHO algorithm are shown in

Sect. 4. Finally, in Sect. 5, the conclusion and possi-

ble future works are presented.

2 VARIANTS OF HHO

2.1 Binary HHO Algorithm

A new binary variant of the HHO is proposed in

(Bes¸kirli and Da

g, 2020) for the binary wind turbine

micrositing problem. The new algorithm is named as

HHO

bin

. The HHO

bin

algorithm proposed for con-

verting the continuous version of the HHO algorithm

into binary version is equipped with ceil (T1), ceil-

Round (T2), ceilFloor (T3), Roundﬂoor (T4), mod-

uleBase A (T5), moduleBase B (T6), T7 and T8 trans-

fer functions. Thus, the original HHO algorithm is

converted to the HHO

bin

algorithm to solve any binary

problems. In this study, the HHO

bin

algorithm is de-

veloped for solving binary wind turbines. In (Thaher

et al., 2020), the HHO is converted into binary using

transfer function to work in the features binary search

space. The main target is to select the best features

from a dataset and enhance the classiﬁcation process.

The used datasets are challenging as they consist of

high dimensionality and a small number of instances.

2.2 Modiﬁed HHO Algorithm

In (G

olc

uk and Ozsoydan, 2020), the HHO algorithm

is proposed to deal with dynamic optimization prob-

lems (DOPs). It is modiﬁed as a multi-population

based algorithm to deal with multiple optima and to

search in different parts of the search space. In (Fan

et al., 2020), a novel quasi-reﬂected Harris hawks al-

gorithm (QRHHO) is proposed, which combines the

HHO algorithm and quasi-reﬂection-based learning

mechanism (QRBL). The QRBL mechanism is pro-

posed to increase the population diversity in the ini-

tial stage, and then, QRBL is added in each popula-

tion position update to improve the convergence rate.

In (Qu et al., 2020), the HHO based on information

exchange is proposed. An individual obtains infor-

mation from the shared area of cooperative foraging

and the location area of collaborators. A nonlinear es-

caping energy factor with chaos disturbance is used to

better balance the local search and the global search.

In (Zhang et al., 2020b), the study focuses on the es-

caping energy (E) of prey. For E, six different update

strategies are used to model the real situation. In (Al-

Betar et al., 2020), the survival-of-the-ﬁttest princi-

ple of evolutionary algorithms is incorporated in the

HHO algorithm. Three selection strategies (i.e., tour-

nament, proportional and linear rank-based methods)

are used in the exploration phase of HHO.

2.3 Hybridizations of HHO Algorithm

In (Abdel-Basset et al., 2020a), the HHO algorithm,

Bitwise operations, and Simulated Annealing are in-

tegrated. The new model is called HHOBSA. The

main purpose is to solve the feature selection prob-

lem. The objective of using the (AND and OR)

is to transfer the best features from the best solu-

tion to other low-quality solutions to raise their ﬁt-

ness. The SA algorithm helps to jump from the lo-

cal minima. Another study that integrated SA algo-

rithm with HHO algorithm is in (Elgamal et al., 2020)

to enhance the exploitation of the HHO algorithm.

In (Attiya et al., 2020), the HHO is also integrated

with SA. In this paper, the authors use SA to per-

form the local search. The proposed method is ap-

plied for scheduling jobs in the cloud environment.

In (Dhawale and Kamboj, 2020), the HHO algorithm

is integrated with Improved Grey wolf optimization.

The model is called hHHO-IGWO. In (Zhao and Gao,

2020), the HHO algorithm is integrated with the slime

mould algorithm to allow the individuals to take more

ways to update their positions. In (Xie et al., 2020b),

the HHO algorithm is used to support the Henry Gas

Solubility Optimization Algorithm (HGSO). Because

the search strategy of HGSO is simple and it is weak

in exploitation, the HHO is proposed.

2.4 Multi-objective HHO Algorithm

In (Du et al., 2020), a multi-objective HHO algorithm

called MOHHO is proposed to estimate the param-

eters of the extreme learning machine (ELM) with

high accuracy and stability in the predication of air

pollution series. In (Y

uzgec¸ and Kusoglu, 2020), in

the MOHHO algorithm, the HHO algorithm struc-

ture is preserved and an archive repository is added to

store and get the Pareto optimal results. The roulette

wheel is used to select the archive member from the

least populated area. The unconstrained test functions

known as ZDT is used to assess the performance of

the proposed model. In (Fu and Lu, 2020), MOHHO

is proposed for the changeable operating conditions

of the hydraulic turbine governing system (HTGS).

Other modiﬁcation strategies are embedded into the

MOHHO including Latin hypercube sampling initial-

ization, modiﬁed differential evolution operator, mu-

tation operator, and nonlinear rabbit energy are ap-

plied in MOHHO (HMOHHO) to enhance the global

Harris Hawks Optimization: A Formal Analysis of Its Variants and Applications

search performance. In (Islam et al., 2020), a new ver-

sion of the HHO algorithm is proposed to solve sin-

gle and multi-objective Optimal Power Flow (OPF)

problems for controlling the emissions from thermal

generating sources. Fuel cost, power loss, and en-

vironmental emissions are used as single and multi-

objective functions for an optimal update of power

system variables.

3 APPLICATIONS OF HHO

3.1 Environment

Malik (Malik et al., 2020), HHO algorithm is used to

optimizes the parameters of support vector regression

(SVR) to accurately and reliably predict the stream-

ﬂow. This helps to manage water resources and pre-

dict reservoir ﬂood. In (Yu et al., 2020), the HHO

algorithm, the random forest (RF), and Monte Carlo

simulation are used to predict, analyze and control

the blast-induced ground vibration. Based on the per-

formance indices, the proposed HHO-RF model can

provide higher prediction performance. The HHO al-

gorithm is used in (Sammen et al., 2020) to predict

scour depth (SD) downstream of the ski-jump spill-

way. The HHO algorithm is applied to improve the

performance of an artiﬁcial neural network (ANN)

to predict the SD. The performance of the ANN-

HHO method is compared with two-hybrid methods,

namely, the particle swarm optimization with ANN

(ANN-PSO) and the genetic algorithm with ANN

(ANN-GA). In (Fu et al., 2020), a new model that

integrates two-layer decomposition, improved hy-

brid differential evolution-HHO (IHDEHHO), phase

space reconstruction (PSR) and kernel extreme learn-

ing machine (KELM) is proposed for the prediction

of the short-term wind speed.

3.2 Manufacturing

In (Saravanakumar and Mohan, 2020), the HHO algo-

rithm is proposed to solve the assembly line balance

problem. The use of the HHO multipoint crossover

method optimizes and improves the performance of a

network-based line balancing problem. The HHO al-

gorithm is proposed in (Jouhari et al., 2020) as a solu-

tion to the scheduling problem. In speciﬁc it is used to

solve unrelated parallel machine scheduling problems

(UPMSPs). The new method, called MHHO, uses

the salp swarm algorithm (SSA) to perform a local

search for HHO. This improves its performance and

decreases its computation time. The MHHO shows

better performance in both small and large problem

cases. In (Golilarz et al., 2019), the HHO algorithm

is proposed in the tuning stage to adjust the convo-

lutional neural network (ConvNet) parameters. This

method is used to enhance the production quality in

the manufacturing industry. The least-squares sup-

port vector machine (LSSVM) is used as a soft-sensor

model to predict key production indicators in complex

grinding processes (Xie et al., 2020a).

3.3 Energy

A Boosted HHO is proposed in (Ridha et al., 2020) to

estimate the parameters of the single diode PV model.

The BHHO algorithm integrates random exploratory

steps of the ﬂower pollination algorithm (FPA) and a

mutation operator of the differential evolution (DE)

with 2-Opt algorithms. In (Mouassa et al., 2020),

the HHO algorithm and other optimizers are used for

energy scheduling in the smart home. A load pre-

diction tool is developed in (Tayab et al., 2020) for

efﬁcient power management in the microgrid. The

paper proposes a hybrid method that compromises

the best-basis stationary wavelet packet transform and

HHO-based feed-forward neural network. The HHO

is used as a training algorithm for optimizing the

weight and basis of neurons of the feed-forward neu-

ral network. Yousri (Yousri et al., 2020), the HHO

algorithm is used to evaluate the best parameters of

the Proportional-Integral (PI) controller simulating

load frequency control (LFC) incorporated in a multi-

interconnected system with renewable energy sources

(RESs). The integral time absolute error (ITAE) of the

frequency and tie-line power is selected as the objec-

tive function. In this study two systems are considered

for testing: the ﬁrst one has two interconnected areas

of thermal and photovoltaic (PV) plants and the sec-

ond system compromises four plants of PV, wind tur-

bine (WT), and two thermal plants considering gover-

nor dead-band (GDB) and generation rate constraint

(GRC).

3.4 Power Systems

The HHO is proposed as a solution to the optimum

power ﬂow (OPF) in (Akdag et al., 2020). This is

an important problem for power system engineering.

The proposed modiﬁed HHO is used to minimize the

fuel cost of the power system when it is applied to

IEEE 30-bus test system. In (Birogul, 2019), the

HHO algorithm is integrated with the mutation op-

erators of Differential Evolution (DE) in a new model

called (HHODE). The HHODE algorithm is proposed

to solve the OPF problem. The effectiveness of the

HHODE hybrid algorithm is tested on a modiﬁed

ECTA 2021 - 13th International Conference on Evolutionary Computation Theory and Applications

IEEE 30-bus test system. The HHO algorithm in (HA

et al., 2019) is used for tuning of integral classical

controllers to design the appropriate load frequency

controllers. The optimized controller parameters and

the enhanced system performance are investigated. It

has a two-area interconnected power system with dif-

ferent participation of DFIG based wind turbine at

the ﬁrst area only. In (Sobhy et al., 2019), the HHO

algorithm is used to determine the gains of the pro-

portional integral derivative (PID) controllers used in

the load frequency control of power systems. The au-

tomatic generation control (AGC) problem is formu-

lated as an objective function using the integral square

of errors (ISE) criterion. The proposed algorithm is

used with a two-area power system that consists of

non reheat thermal generating units that consider the

generation rate constraints (GRC).

3.5 Feature Selection

Feature selection is a datamining technique to select

the most informative features in a dataset (Alazab

et al., 2012). In (Sihwail et al., 2020), the HHO al-

gorithm is modiﬁed and introduced as a search algo-

rithm for feature selection. The HHO algorithm is

modiﬁed and improved for feature selection in (Elga-

mal et al., 2020). Using different evaluation criteria,

it was compared and analyzed. In (Thaher and Ar-

man, 2020), the HHO algorithm is used as a search

strategy to ﬁnd a near-optimal solution for the fea-

ture selection problem. Different classiﬁers are used

to assess the found solutions: K-nearest neighbors

(kNN), Decision Trees (DT), and Linear Discriminant

Analysis (LDA). The standard continuous HHO algo-

rithm is converted into binary in (Too et al., 2019)

using S and V transfer functions and another variant

of HHO called quadratic HHO is generated. In (Is-

mael et al., 2020), the HHO is applied for optimiz-

ing the hyperparameter of v-support vector regression

(v-SVR) and simultaneously performs feature selec-

tion. The HHO algorithm is modiﬁed and hybridized

in (Abdel-Basset et al., 2020a) to enhance its perfor-

mance for the feature selection problem. In (Zhang

et al., 2020a), the HHO algorithm in this paper is en-

hanced to get high-quality solutions for solving the

feature selection problem.

3.6 Medical Applications

The segmentation of medical images is presented in

(Rodr

ıguez-Esparza et al., 2020). Authors in this

paper, propose the HHO algorithm to identify the

regions of interests (ROIs) that contain malignant

masses. In the applied multilevel threshold segmen-

tation technique, the minimum cross-entropy thresh-

olding (MCET) is used to select the optimal threshold

values for the segmentation. (Rammurthy and Ma-

hesh, 2020). The model is based on using cellular au-

tomata and rough set theory to make segmentation for

the MR images. A deep convolutional neural network

(DeepCNN) is used for brain tumor detection. The

training is carried out using the integrating of Whale

Optimization and HHO algorithms (WHHO). A vari-

ant of the HHO algorithm is used for collecting the

most informative features regarding the mammogra-

phy in (Hans et al., 2020). The HHO algorithm is

integrated with Support Vector Machines (SVM) and

the k-Nearest Neighbors (k-NN) in (Houssein et al.,

2020a). The two methods HHOSVM and HHO-kNN

are used for chemical descriptor selection and chem-

ical compound activities. A hybrid HHO algorithm

in (Houssein et al., 2020b) is used with two opera-

tors: cuckoo search (CS) and chaotic maps. The sup-

port vector machines (SVMs) are then used by the

proposed CHHO–CS as an objective function for the

classiﬁcation process. The CHHO–CS-SVM is tested

in the selection of appropriate chemical descriptors

and compound activities.

3.7 Network Applications

A new model based on hybrid HHO and salp

swarm (HH-SS) optimization algorithm is proposed

in (Srinivas and Amgoth, 2020). The main motiva-

tion is to increase the lifetime of the wireless sen-

sor network (WSN) using energy-efﬁcient optimiza-

tion methods. The hybrid HH-SS algorithm is uti-

lized to select a single cluster head after dividing

the WSN into a set of clusters using the K-medoid

clustering approach. In (Houssein et al., 2020c), the

HHO algorithm is used to determine the location of

the sink node in the large-scale wireless sensor net-

work (LSWSN). The sink node is responsible to col-

lect and process the information from the sensor node

and control the entire network. Hence, determining

the position of the sink node will affect the lifetime

of the network. The HHO algorithm in (Diaaeldin

et al., 2020) is used to solve the mixed-integer non-

linear programming problem (MINLP). The proposed

method reduced the total active losses by 21.428%.

In (Abdel-Basset et al., 2020b), the HHO algorithm

is used with the local search (HHOLS) as an energy-

aware method for task scheduling in fog computing

(TSFC) to enhance the service provided to users in the

industrial internet of things (IIOT) applications. The

HHO algorithm is used for the ﬁrst time to solve In-

telligent Reﬂecting Surface (IRS) network optimiza-

tion problem in (Huaqiang et al., 2020). In (Bhat

Harris Hawks Optimization: A Formal Analysis of Its Variants and Applications

and Venkata, 2020), the HHO algorithm is used to

solve the localization problem with area minimiza-

tion (HHO-AM). The HHO algorithm in (Singh and

Prakash, 2020) is used in the hybrid ﬁber-wireless

(FiWi) access network to determine the optimal place-

ment of Optical Network Units (ONUs) in FiWi net-

work. The results show the superiority of the HHO

algorithm over other algorithms.

3.8 Engineering

A new method based on the HHO algorithm and

ﬁrst-order reliability method (FORM) is proposed in

(Zhong et al., 2020). HHO-FORM is used to solve

the high-dimensional reliability problem. The HHO-

FORM is evaluated based on three numerical high-

dimensional problems and two high-dimensional en-

gineering problems to test its performance. In

(Barshandeh et al., 2020), the HHO and artiﬁcial

ecosystem-based optimization (AEO) algorithms are

hybridized. The performance of the proposed method

is studied on constrained/unconstrained real-life en-

gineering problems. A fault diagnosis method for the

rolling bearing is proposed in (Shao et al., 2020). The

new method is based on integrating variational mode

decomposition (VMD), time-shift multiscale disper-

sion entropy (TSMDE), and support vector machine

(SVM) optimized by vibrational Harris hawks op-

timization algorithm (VHHO). The HHO algorithm

has been proposed to achieve optimal parameters of

a PID controller for aircraft pitch control system in

(Izci et al., 2020). In (Nalcaci et al., 2020), the HHO

is used for harmonic elimination in a traction motor

drive based voltage source inverter. It is applied to a

two-level, three-phase inverter to solve the equations.

The proposed method has higher accuracy and con-

vergence than the grey wolf optimizer (GWO). The

authors in (Ekinci et al., 2020) applied the HHO algo-

rithm to tune a PID controller for the regulation of the

speed of a DC motor. The proposed method is used to

minimize the integral of time multiplied absolute er-

ror (ITAE) and obtain optimal parameters of the PID

controller.

3.9 Image Processing

In (Jia et al., 2020), pulse coupled neural network

(PCNN) is used for image segmentation. HHO is used

to search simpliﬁed PCNN parameters to reduce the

number of parameters without affecting the segmen-

tation effect. Then, image entropy (H) and mutual

information entropy (MI) are used as ﬁtness func-

tions. In (Wunnava et al., 2020), a modiﬁed HHO

algorithm is used as a maximization search algorithm

for the segmentation of images. It is used to obtain

the optimal threshold values when the 2D grey gra-

dient (I2DGG) multilevel thresholding method is ap-

plied. In (Shahid et al., 2019), the HHO algorithm

is used in combination with the thresholding func-

tion to obtain the best parameters of the thresholding

function for image denoising in the wavelet domain.

In (Golilarz et al., 2020), an improved version of the

HHO algorithm called (CMDHHO) is used. HHO is

assisted with differential evolution, chaos, and multi-

population for satellite image denoising in the wavelet

domain.

4 DISCUSSION

As reviewed earlier, the HHO algorithm has been

adopted to address many optimization problems since

it was proposed.The source codes of HHO and related

supplementary materials are publicly available at http:

//www.aliasgharheidari.com/HHO.html, http://www.

alimirjalili.com/HHO.html and http://www.evo-ml.

com/2019/03/02/hho. The simple structure, few pa-

rameters, and adaptive and time varying search are the

essential issues for the wide-spread usage of this algo-

rithm. However, it has several limitations and suffers

from some shortcomings. The advantages of the HHO

algorithm include:

• There is a single control energy parameter E

which plays a vital role in balancing the exploita-

tion and exploration of the algorithm

• Solves a variety of complex problems

• Simple structure

• Easy to implement

• The adaptive and time-varying parameters allows

HHO to overcome difﬁculties of a search space

such as local minima

The disadvantages of the HHO algorithm include:

• Immature balance between exploitation and ex-

ploration

• Can’t guarantee that it will not fall into the local

minima trough the process of searching.

• The performance of the HHO decreases when in-

creasing the dimensions of the problem

• Slow convergence

• Low diversity

• Exploration ability (single search method) is

weaker than its exploitation ability

ECTA 2021 - 13th International Conference on Evolutionary Computation Theory and Applications

5 CONCLUSION

In this survey, over 100 research papers are col-

lected about the HHO algorithm. The main goal is

to study, analyze, and discuss the main features, ad-

vantages, disadvantages of the HHO optimizer as a

new meta-heuristic optimization algorithm. This re-

view summarizes references published after the de-

velopment of the HHO algorithm in 2019 until the

beginning of 2020 (December- 2020). Most of these

papers described the variants of the HHO algorithm

to tackle different optimization problems. Further-

more, discusses the applications of the HHO algo-

rithm in various domains. The HHO algorithm has

been widely adopted to solve various optimization

problems. However, there is still room for improve-

ment to enhance its performance. The HHO could be

further hybridized, modiﬁed, and improved. In future

work, we will cover the following perspectives:

• Using the HHO to solve optimization problems,

especially multi-objective optimization problems.

• Hybridizing the HHO with other algorithm trajec-

tory and population-based algorithms.

ACKNOWLEDGMENTS

This work is supported by the Ministerio espa

nol

de Econom

ıa y Competitividad under projects

TIN2017-85727-C4-2-P (UGR-DeepBio) and

PID2020-115570GB-C22 (DemocratAI::UGR).

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