Modified Rat Swarm Optimization Based Localization Algorithm for Wireless Sensor Networks

Alfawaz, Oruba; Osamy, Walid; Saad, Mohamed; Khedr, Ahmed M.

doi:10.1007/s11277-023-10347-x

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…Rat swarm optimization (RSO) is an innovative bio-inspired algorithm, introduced by Gaurav Dhiman et al (2020), which mimics the hunting and fighting behaviors of rats in nature to emulate their social intelligence and aggressiveness, effectively addressing global optimization challenges [24]. Oruba et al ( 2023) propose a modified rat swarm optimization algorithm for node localization in wireless sensor networks, resulting in significantly reduced positioning error [25]. Walid at al.…”

Section: Referencesmentioning

confidence: 99%

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Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

Wei,

Xiao,

Wang

2024

Mathematics

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The vehicle routing problem with time windows (VRPTW) remains a formidable challenge, due to the intricate constraints of vehicle capacity and time windows. As a result, an algorithm tailored for this problem must demonstrate robust search capabilities and profound exploration abilities. Traditional methods often struggle to balance global search capabilities with computational efficiency, thus limiting their practical applicability. To address these limitations, this paper introduces a novel hybrid algorithm known as large neighborhood search with modified rat swarm optimization (LNS-MRSO). Modified rat swarm optimization (MRSO) is inspired by the foraging behavior of rat swarms and simulates the search process for optimization problems. Meanwhile, large neighborhood search (LNS) generates potential new solutions by removing and reinserting operators, incorporating a mechanism to embrace suboptimal solutions and strengthening the algorithm’s prowess in global optimization. Initial solutions are greedily generated, and five operators are devised to mimic the position updates of the rat swarm, providing rich population feedback to LNS and further enhancing algorithm performance. To validate the effectiveness of LNS-MRSO, experiments were conducted using the Solomon VRPTW benchmark test set. The results unequivocally demonstrate that LNS-MRSO achieves optimal solutions for all 39 test instances, particularly excelling on the R2 and RC2 datasets with percentage deviations improved by 5.1% and 8.8%, respectively, when compared to the best-known solutions (BKSs). Furthermore, when compared to state-of-the-art algorithms, LNS-MRSO exhibits remarkable advantages in addressing VRPTW problems with high loading capacities and lenient time windows. Additionally, applying LNS-MRSO to an unmanned concrete-mixing station further validates its practical utility and scalability.

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Section: Referencesmentioning

confidence: 99%

“…The formula utilizes M to represent the scale of customers, while c represents a constant with an approximate value of 18.22. This constant is derived from the calculation 5(log 2 25 − 1). By utilizing this formula, we can determine suitable TR (threshold or performance metric) values for different instance scales.…”

Section: Parameter Tr Tuningmentioning

confidence: 99%

Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

Wei,

Xiao,

Wang

2024

Mathematics

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“…The TN is used to control distance as di = d i + n i , where d i signifies the actual distance, viz., calculated amongst the place of beacon (x i , y i ) and the place of TN (x, y). It is defined in Equation (13).…”

Section: Steps Involved In Cmloa-nla Techniquementioning

confidence: 99%

“…Previously, several challenging techniques have been employed to face the difficulty of WSN node localization (NL) [12]. Some bio-inspired methods for localization are presented that can also be analyzed in this paper, namely a bat optimization algorithm (BA), swarm-based algorithms, evolutionary algorithms, and others stimulated by animal social activity [13]. It is a new technique for emerging innovative computing algorithms that depend on natural development [14].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks

Motwakel,

Hashim,

Alamro

et al. 2023

Sensors

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Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

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3D localization of wireless sensor IoT nodes based on weighted DV‐Hop algorithm

Zhang,

Cui,

Yan

2024

Adv Control Appl

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With the widespread popularity of smart wearable devices and the rise of emerging Internet of Things applications, such as smart cities, smart homes, and smart cars, the demand for Internet of Things devices is growing. The technology for positioning Internet of Things nodes using traditional wireless sensors only provides approximate location information, which is insufficient for high‐precision applications. To achieve accurate sensor node location in a specific area, this study proposes an advanced weighted distance vector jump location algorithm. This paper proposes using optical wireless networks, a new wireless communication technology, to enhance the distance vector jump algorithm. It is considered the core technology in researching the three‐dimensional positioning of wireless sensor IoT nodes. The experimental data validated that by comparing with existing positioning algorithms, the improved algorithm significantly improved the location accuracy, and its average orientation error was significantly lower than other algorithms. In three cases where the wireless sensor communication radius was between 10 and 30 m, the average positioning errors of the improved algorithm were 0.363, 0.264, and 0.258, respectively. Compared with the pre improved Distance Vector Hop algorithm, its accuracy has increased by 41.1%, indicating the better positioning performance. Overall, the improved weighted algorithm significantly improves the positioning effect, providing strong technical support for the three‐dimensional positioning of wireless sensor Internet of Things nodes.

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Modified Rat Swarm Optimization Based Localization Algorithm for Wireless Sensor Networks

Cited by 11 publications

References 51 publications

Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks

3D localization of wireless sensor IoT nodes based on weighted DV‐Hop algorithm

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