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A systematic review on emperor penguin optimizer

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Abstract

Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold. Firstly, EPO has low learning curve (i.e., based on the simple analogy of huddling behavior of emperor penguins in nature (i.e., surviving strategy during Antarctic winter). Secondly, EPO offers straightforward implementation. In the EPO, the emperor penguins represent the candidate solution, huddle denotes the search space that comprises a two-dimensional L-shape polygon plane, and randomly positioned of the emperor penguins represents the feasible solution. Among all the emperor penguins, the focus is to locate an effective mover representing the global optimal solution. To-date, EPO has slowly gaining considerable momentum owing to its successful adoption in many broad range of optimization problems, that is, from medical data classification, economic load dispatch problem, engineering design problems, face recognition, multilevel thresholding for color image segmentation, high-dimensional biomedical data analysis for microarray cancer classification, automatic feature selection, event recognition and summarization, smart grid system, and traffic management system to name a few. Reflecting on recent progress, this paper thoroughly presents an in-depth study related to the current EPO’s adoption in the scientific literature. In addition to highlighting new potential areas for improvements (and omission), the finding of this study can serve as guidelines for researchers and practitioners to improve the current state-of-the-arts and state-of-practices on general adoption of EPO while highlighting its new emerging areas of applications.

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Abbreviations

ABC:

Artificial Bee Colony

ACLD:

Adaptive Cross-Layer Design

ACO:

Ant Colony Optimizer

ADTF:

Adaptive Dual Threshold Filter

AFD:

Adaptive Fourier Decomposition

ASMF:

Adaptive Switching Mean Filter

BA:

Bat Algorithm

CFA:

Cultural Firework Algorithm

CGAMO:

Chaotic Multi-objective GA

CMOPSO:

Chaotic Multi-objective PSO

CMSaVD:

Chaotic Map and Sample Value Difference

CSA:

Crow Search Algorithm

DS:

Differential Search Algorithm

DWT:

Discrete Wavelet Transform

EIWO:

Improved Invasive Weed Optimization

EMD:

Empirical Mode Decomposition

EEMD:

Ensemble EMD

EPO:

Emperor Penguin Optimization

EPOSH:

EPO Self-Healing

EPOUA:

EPO User Association

EPSEO:

Emperor Penguin and Social Engineering Optimizer

FIDM:

Fuzzy Intelligent Decision Making

FLC:

Fuzzy Logic Controller

FPA:

Flower Pollination Algorithm

GA:

Genetic Algorithm

GenClustMOO:

Multi-objective Clustering Technique

GSA:

Gravitational Search Algorithm

GWO:

Grey Wolf Optimizer

HDEPO:

Hybrid Deep Emperor Penguin Optimizer

HDNN:

Hybrid Deep Neural Network

IDSA:

Improved Differential Search Algorithm

IWO:

Invasive Weed Optimization

LMVO:

Multiverse Optimization Algorithm based on Lévy flight

MABC:

Modified ABC

MFO:

Moth-Flame Optimization

MO-:

Multi-objective

MOCK:

MO clustering with automatic K determination

MOEA/D:

MO Evolutionary Algorithm based on Decomposition

MVO:

Multi-verse Optimizer

NN:

Neural Network

NSGA-II:

Non-dominated Sorting Genetic Algorithm

PESA-II:

Pareto Envelope Selection Algorithm

QEPO:

Quantum-based EPO

RETP-:

Reliable ECG Transmission Protocol-

FD1/2/3:

Doppler Frequency 1/2/3

SA:

Simulated Annealing

SCA:

Sine Cosine Algorithm

SHO:

Spotted hyena Optimizer

SPEA2:

Strength Pareto Evolutionary Algorithm

SSA:

Salp Swarm Algorithm

SVD:

Singular Value Decomposition

SVM:

Support Vector Machine

TDQ:

Time-Domain Quantizer

TLBO:

Teaching-Learning-Based Optimization

WCO:

World Cup Optimization

WOA:

Whale Optimization Algorithm

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Funding

The work reported in this paper is funded by the Trans-Disciplinary Research Grant Scheme from the Ministry of Higher Education Malaysia titled: An Artificial Neural Network Sine Cosine Algorithm-based Hybrid Prediction Model for the Production of Cellulose Nanocrystals from Oil Palm Empty Fruit Bunch (RDU1918014).

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Authors and Affiliations

  1. Faculty of Computing, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    Md. Abdul Kader & Kamal Z. Zamli

  2. Department of Mathematics and Computer Science, Karlstad University, 651 88, Karlstad, Sweden

    Bestoun S. Ahmed

  3. Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Karlovo nam. 13, 121 35, Praha 2, Czech Republic

    Bestoun S. Ahmed

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  1. Md. Abdul Kader
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Correspondence to Kamal Z. Zamli.

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Kader, M.A., Zamli, K.Z. & Ahmed, B.S. A systematic review on emperor penguin optimizer. Neural Comput & Applic 33, 15933–15953 (2021). https://doi.org/10.1007/s00521-021-06442-4

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