Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

Al‐Khateeb, Belal; Ahmed, K.A.; Mahmood, Maha; Le, Dac‐Nhuong

doi:10.32604/cmc.2021.013648

Cited by 26 publications

(21 citation statements)

References 22 publications

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“…It is worth mentioning that there is other recently developed metaheuristic algorithms motivated by swarmbased techniques, some of the famous ones include: Coyote Optimization Algorithm (COA) inspired on the Canis latrans species [11], Sea Lion Optimization (SlnO) algorithm inspired by the natural hunting behavior of sea lions' whiskers [154], Butterfly Optimization Algorithm (BOA) inspired natural behavior of butterflies for food search and mating [155], Monarch Butterfly Optimization (MBO) inspired the natural migration skills of monarch butterflies [156], SailFish Optimizer (SFO) inspired by the natural hunting skills of sailfish [157], Emperor Penguins Colony (EPC) mimics the natural behavior of emperor penguins [158], Manta Ray Foraging Optimization Algorithm (MRFOA) inspired from the natural behavior of manta rays based on three foraging strategies [159], Tunicate Swarm Algorithm (TSA) inspired the behavior of tunicates [160], Black Widow Optimization Algorithm (BWOA) that mimics the social courtship behavior of black widow spiders [161], African Vultures Optimization Algorithm (AVOA) inspired by the natural behavior of African vultures for foraging and navigation [162], Red Colobuses Monkey (RCM) algorithm mimics the natural behavior of red monkeys [163], Rock Hyraxes Swarm Optimization (RHSO) inspired by the natural behavior of rock hyraxes swarms [164], Artificial Gorilla Troops Optimizer (GTO) inspired by the natural gorilla troops' social intelligence [165], Ebola Optimization search Algorithm (EOSA) mimics the natural propagation mechanism of Ebola virus disease [166], Dingo Optimizer (DOX) that inspired by the social behavior of dingo [167], Honey Badger Algorithm (HBA) inspired the natural foraging behavior of honey badger [168], and so on. However, none of these newly swarm-based algorithms were adopted in t-way testing addressing combinatorial optimization problem.…”

Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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“…The findings show that the proposed model outperforms comparable algorithms on most benchmark functions, especially high‐dimensional issues. In another work, Al‐Khateeb et al proposed a novel MH algorithm, Rock Hyraxes swarm optimization (RHSO), inspired by the natural behaviour of rock hyrax swarms (Al‐Khateeb et al, 2021). Rock hyraxes live in colonies or groups with a dominant male, which guards the entire colony.…”

Section: Related Workmentioning

confidence: 99%

Analysing stable feature selection through an augmented marine predator algorithm based on opposition‐based learning

2021

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“…Creatures that live collectively in nature have optimal behavior with others to achieve their purpose, and modeling their behavior leads to important optimization algorithms. Particle Swarm Optimization (PSO) [ 16 ], Cuckoo Optimization Algorithm (COA) [ 17 ], Whale Optimization Algorithm (WOA) [ 18 ], combined Particle Filter and Particle Swarm Optimization (PF-PSO) [ 19 ], Tuna Swarm Optimization (TSO) [ 20 ], and Rock Hyraxes Swarm Optimization (RHSO) [ 21 ] are the most popular optimization algorithms in this category. Evolutionary algorithms : Many biological processes have evolved during the time, and several metaheuristic algorithms are proposed by modeling them.…”

Section: Introductionmentioning

confidence: 99%

Ladybug Beetle Optimization algorithm: application for real-world problems

Safiri

Nikoofard

2022

J Supercomput

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In this paper, a novel optimization algorithm is proposed, called the Ladybug Beetle Optimization (LBO) algorithm, which is inspired by the behavior of ladybugs in nature when they search for a warm place in winter. The new proposed algorithm consists of three main parts: (1) determine the heat value in the position of each ladybug, (2) update the position of ladybugs, and (3) ignore the annihilated ladybug(s). The main innovations of LBO are related to both updating the position of the population, which is done in two separate ways, and ignoring the worst members, which leads to an increase in the search speed. Also, LBO algorithm is performed to optimize 78 well-known benchmark functions. The proposed algorithm has reached the optimal values of 73.3% of the benchmark functions and is the only algorithm that achieved the best solution of 20.5% of them. These results prove that LBO is substantially the best algorithm among other well-known optimization methods. In addition, two fundamentally different real-world optimization problems include the Economic-Environmental Dispatch Problem (EEDP) as an engineering problem and the Covid-19 pandemic modeling problem as an estimation and forecasting problem. The EEDP results illustrate that the proposed algorithm has obtained the best values in either the cost of production or the emission or even both, and the use of LBO for Covid-19 pandemic modeling problem leads to the least error compared to others.

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Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

Cited by 26 publications

References 22 publications

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Analysing stable feature selection through an augmented marine predator algorithm based on opposition‐based learning

Ladybug Beetle Optimization algorithm: application for real-world problems

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