Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems

Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Elaziz, Mohamed Abd

doi:10.1007/s00500-021-05889-w

Cited by 29 publications

(10 citation statements)

References 41 publications

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“…The author also applied the hill-climbing method during BA evaluation. The proposed algorithm outperformed the PSO algorithm in all experimental cases [ 55 ]. Moreover, BA achieved optimality when the input was small numerical values, regardless of the size of the problem obtained.…”

Section: Bat Algorithm Variantsmentioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

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Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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Section: Bat Algorithm Variantsmentioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…A modified WOA for the problem considering setup times and makespan minimisation is investigated in [259]. In his study the WOA is combined with the FA in a way that each individual (depending on its fitness) is either modified using the operators from WOA or FA.…”

Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel machines scheduling problem (UPMSP), which is only one of the many different problem types that exist, found its application in many areas like production industries or distributed computing. Due to the complexity of the problem, heuristic and metaheuristic methods are gaining more attention for solving it. Although this problem variant did not receive much attention as other models, recent years saw the increase of research dealing with this problem. During that time, many different problem variants, solution methods, or other interesting research directions were considered. However, no study has until now tried to systematise the research in which heuristic methods are applied for the UPMSP. The goal of this study is to provide an extensive literature review on the application of heuristic and metaheuristic methods for solving the UPMSP. The research was systematised and classified into several categories to enable an easy overview of the different problem and solution variants. Additionally, current trends and possible future research directions are also shortly outlined.

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“…In literature, there are different approaches proposed for IDS by improving new FS methods that boosted the efficiency of the IDS. For example, grey wolf optimizer (GWO) [ 14 , 15 ], crow search algorithm (CSA) [ 16 ], genetic algorithm (GA) [ 17 , 18 , 19 ], whale optimization algorithm [ 20 ], random harmony search (RHS) [ 21 ], and also the well-known, particle swarm optimization (PSO) [ 22 ]. Although these approaches showed significant performance, they suffer from certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani

Dahou

Al-qaness

et al. 2021

Sensors

Self Cite

123

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Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

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Modified whale optimization algorithm for solving unrelated parallel machine scheduling problems

Cited by 29 publications

References 41 publications

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

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