A Review of Metaheuristic Optimization Algorithms in Wireless Sensor Networks

Houssein, Essam H.; Saad, Mohammed R.; Hussain, Kashif; Shaban, Hassan; Hassaballah, M.

doi:10.1007/978-3-030-70542-8_9

Cited by 5 publications

(2 citation statements)

References 121 publications

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“…Thus compared to other optimization methods, metaheuristics as PSO [23], BA [24], or HHO [25], have unique and exceptional mathematical qualities. As specified by Siarry P [26] and Cuevas E et al [11], metaheuristics allow the resolution of a very wide range of optimization problems, too complex for "conventional" optimization methods, even if the problems are non-linear, discrete or continuous, nonconvex, non-differentiable, unimodal or highly multimodal, high-dimensional with non-smooth constraints and noisy [27][28][29][30][31]. Moreover, their algorithmic strategies allow them to analyze the search space with a oscillation correctly balanced between exploration and exploitation [31].…”

Section: Introductionmentioning

confidence: 99%

“…Thus 𝑓(𝑥) is like a "breadcrumb trail" for metaheuristics in the search for the global optimum of the optimization problem. Thereby, metaheuristics see optimization problems as a "black box" regardless of its content [27,29,30]. This asset makes it possible to design complex systems based on deep learning algorithms on the condition of mathematically and correctly modeling the entire system by one (mono-objective) or several (multi-objective) objective functions to be optimized.…”

Section: Introductionmentioning

confidence: 99%

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BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

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Faced with the increase in high-dimensional Big Data creating more volume and complexity, the feature selection process became an essential phase in the preprocessing workflow upstream of the design of systems based on deep learning. This paper is a concrete and first application of the new metaheuristic Harris Hawk Optimization Encirclement-Attack-Synergy (HHO-EAS) in solving the NP-Hard wrapper feature selection multi-objective optimization problem. This problem combines two contradictory objectives: maximizing the accuracy of a classifier while minimizing the number of the most relevant and non-redundant selected features. To do this we hybridized HHO-EAS to create the new metaheuristic Binary HHO-EAS (BHHO-EAS). We combined HHO-EAS to the sixteen transfer functions most used in the literature structured in a balanced way among the four main categories including S-Shaped, V-Shaped, Q-Shaped and U-Shaped. This wide range of transfer function allows us to analyze the evolution of BHHO-EAS’s skills according to the assigned transfer function and to determine which of them offer the best performances. We applied wrapper feature selection to the well-known NSL-KDD dataset with the deep learning Multi Layer Perceptron (MLP) classifier. We put BHHO-EAS in competition with three other well-known population based binary metaheuristics, BPSO, BBA and BHHO. The analysis of the experimental results, compared to the three other binary metaheuristics, demonstrated that BHHO-EAS obtained the best performance on 100% of the transfer functions. This is more particularly highlighted by the U-Shaped transfer functions, which give an acceptable compromise for the two objectives of the problem with an average accuracy of 96,4% and an average size of selected features of 20.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

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Recent Trends in Face Recognition Using Metaheuristic Optimization

Chalabi

Attia

Bouziane

et al. 2022

Studies in Systems, Decision and Control

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Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challenges

Houssein,

Saad,

Djenouri

et al. 2024

Cluster Comput

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Metaheuristic algorithms have wide applicability, particularly in wireless sensor networks (WSNs), due to their superior skill in solving and optimizing many issues in different domains. However, WSNs suffer from several issues, such as deployment, localization, sink node placement, energy efficiency, and clustering. Unfortunately, these issues negatively affect the already limited energy of the WSNs; therefore, the need to employ metaheuristic algorithms is inevitable to alleviate the harm imposed by these issues on the lifespan and performance of the network. Some associated issues regarding WSNs are modelled as single and multi-objective optimization issues. Single-objective issues have one optimal solution, and the other has multiple desirable solutions that compete, the so-called non-dominated solutions. Several optimization strategies based on metaheuristic algorithms are available to address various types of optimization concerns relating to WSN deployment, localization, sink node placement, energy efficiency, and clustering. This review reports and discusses the literature research on single and multi-objective metaheuristics and their evaluation criteria, WSN architectures and definitions, and applications of metaheuristics in WSN deployment, localization, sink node placement, energy efficiency, and clustering. It also proposes definitions for these terms and reports on some ongoing difficulties linked to these topics. Furthermore, this review outlines the open issues, challenge paths, and future trends that can be applied to metaheuristic algorithms (single and multi-objective) and WSN difficulties, as well as the significant efforts that are necessary to improve WSN efficiency.

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A Review of Metaheuristic Optimization Algorithms in Wireless Sensor Networks

Cited by 5 publications

References 121 publications

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

Recent Trends in Face Recognition Using Metaheuristic Optimization

Metaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challenges

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