A Comparative Study between ACO and DE Techniques by Numerical Functions Optimization

Abi, Soufiane; Benhala, Bachir; Bouyghf, Hamid; Fakhfakh, Mourad

doi:10.1109/icoa.2019.8727614

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…The behaviour of real ants and sensor nodes share similar attributes. Thus, the algorithm has been applied to solve many routing problems in WSNs [29][30][31].…”

Section: A the Significance Of The Routing Protocol For Wireless Senmentioning

confidence: 99%

An Improved Ant Colony Optimization Algorithm: A Technique for Extending Wireless Sensor Networks Lifetime Utilization

Abidoye¹,

Ochola²,

Obagbuwa³

et al. 2020

IJACSA

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Wireless sensor networks (WSNs) are one of the most essential technologies in the 21st century due to their increase in various application areas and can be deployed in areas where cable and power supply are difficult to use. However, sensor nodes that form these networks are energyconstrained because they are powered by non-rechargeable small batteries. Thus, it is imperative to design a routing protocol that is energy efficient and reliable to extend network lifetime utilization. In this article, we propose an improved ant colony optimization algorithm: a technique for extending wireless sensor networks lifetime utilization called AMACO. We present a new clustering method to avoid the overhead that is usually involved during the election of cluster heads in the previous approaches and energy holes within the network. Moreover, fog computing is integrated into the scheme due to its ability to optimize the limited power source of WSNs and to scale up to the requirements of the Internet of Things applications. All the data packets received by the fog nodes are transmitted to the cloud for further analysis and storage. An improved ant colony optimization (ACO) algorithm is used to construct optimal paths between the cluster heads and fog nodes for a reliable end-to-end data packets delivery. The simulation results show that the network lifetime in AMACO increased by 22.0%, 30.7%, and 32.0% in comparison with EBAR, IACO-MS, and RRDLA before the first node dies (FND) respectively. It increased by 15.2%, 18.4%, and 33.5% in comparison with EBAR, IACO-MS, and RRDLA before half nodes die (HND) respectively. Finally, it increased by 28.2%, 24.9%, and 58.9% in comparison with EBAR, IACO-MS, and RRDLA before the last node dies (LND) respectively.

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“…The behaviour of real ants and sensor nodes share similar attributes. Thus, the algorithm has been applied to solve many routing problems in WSNs [29][30][31].…”

Section: A the Significance Of The Routing Protocol For Wireless Senmentioning

confidence: 99%

An Improved Ant Colony Optimization Algorithm: A Technique for Extending Wireless Sensor Networks Lifetime Utilization

Abidoye¹,

Ochola²,

Obagbuwa³

et al. 2020

IJACSA

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“…The neighbouring distances increase progressively, since fog nodes already on the chain cannot be revisited. Equation (17) is used to connect nodes to their closest neighbours. A fog node with the shortest distance to the cloud computing in Tier 2 is selected as the leader, and its main duty is to transmit aggregated data to the cloud computing.…”

Section: Data Transmission Between Fog Nodes and Data Centrementioning

confidence: 99%

“…They deposit a chemical substance called pheromone on their path as they traverse the network. This method has been used to solve many optimization problems in WSNs [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient hierarchical routing in wireless sensor networks based on Fog Computing

Abidoye

Kabaso

2020

Preprint

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Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the 21st century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize energy consumption of sensor nodes so as to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called Energy-Efficient Hierarchical routing protocol for wireless sensor networks based on Fog Computing (EEHFC). Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of Things applications. In addition, we propose an improved ant colony optimization (ACO) algorithm that can be used to construct optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses and extends the network lifetime.

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“…Severals meta-heuristic techniques have been proposed in the literature, such as genetic algorithm (GA) [7,8] and Differential Evolution [9][10][11]. Among the metaheuristics offering the best results are those inspired by nature.…”

Section: Introductionmentioning

confidence: 99%

An optimal design of square spiral integrated inductor using metaheuristic techniques

Abi

Bouyghf

Benhala

et al. 2020

IJEECS

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<p>In this paper, the optimal sizing of CMOS RF square spiral integrated inductor utilizing three meta-heuristic techniques namely Ant Colony Optimization, Artificial Bee Colony and Differential Evolution is presented. The π-model is employed for the characterization of inductor behavior. In this optimization procedure, the geometrical parameters of the CMOS RF square spiral integrated inductor are considered as the design variables that satisfy the most important constraints such as the fixed value of required inductance 4nH at the operating frequency 2.4 GHz. The design of the integrated square spiral inductor is done with UMC 130 nm CMOS technology. A comparison between the used meta-heuristic techniques is emphasized. The optimization results are checked and validated by the mean of the Momentum Advanced Design System (ADS).</p>

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A Comparative Study between ACO and DE Techniques by Numerical Functions Optimization

Cited by 9 publications

References 9 publications

An Improved Ant Colony Optimization Algorithm: A Technique for Extending Wireless Sensor Networks Lifetime Utilization

An Improved Ant Colony Optimization Algorithm: A Technique for Extending Wireless Sensor Networks Lifetime Utilization

Energy-efficient hierarchical routing in wireless sensor networks based on Fog Computing

An optimal design of square spiral integrated inductor using metaheuristic techniques

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