A Privacy-sensitive Service Selection Method Based on Artificial Fish Swarm Algorithm in the Internet of Things

Jia, Bing; Hao, Lifei; Zhang, Chuxuan; Huang, Baoqi

doi:10.1007/s11036-019-01488-0

Cited by 5 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The AF model is made up of two parts: variables and functions. The variables section contains the following items: 𝑋 denotes the current position of AF, which is represented as an array 𝑋 = [𝑥1, 𝑥2 … 𝑥𝑛], step denotes the maximum length of the movement step, and the functions component covers three AF behaviors: preying behavior, swarming behavior, and following behavior [24][25][26][27] . Visual refers to the visual distance of AF, try number is the maximum number of searching tours within the visible distance, and a crowd factor (δ) (0 < 𝛿 < 1).…”

Section: Materials and Methods: Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem

Abdulqader

Ali

2023

Baghdad Sci.J

View full text Add to dashboard Cite

Artificial fish swarm algorithm (AFSA) is one of the critical swarm intelligent algorithms. In this paper, the authors decide to enhance AFSA via diversity operators (AFSA-DO). The diversity operators will be producing more diverse solutions for AFSA to obtain reasonable resolutions. AFSA-DO has been used to solve flexible job shop scheduling problems (FJSSP). However, the FJSSP is a significant problem in the domain of optimization and operation research. Several research papers dealt with methods of solving this issue, including forms of intelligence of the swarms. In this paper, a set of FJSSP target samples are tested employing the improved algorithm to confirm its effectiveness and evaluate its execution. Finally, this paper concludes that the enhanced algorithm via diversity operators has discrepancies about the initial AFSA, and it also provided both sound quality resolution and intersected rate.

show abstract

Section: Materials and Methods: Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem

Abdulqader

Ali

2023

Baghdad Sci.J

View full text Add to dashboard Cite

show abstract

Service discovery and selection in IoT: A survey and a taxonomy

Achir

Abdelli

Mokdad

et al. 2022

Journal of Network and Computer Applications

View full text Add to dashboard Cite

A new QoS-aware service discovery technique in the Internet of Things using whale optimization and genetic algorithms

Liu,

Deng

2024

J. Eng. Appl. Sci.

View full text Add to dashboard Cite

Rapid technological advances have made daily life easier and more convenient in recent years. As an emerging technology, the Internet of Things (IoT) facilitates interactions between physical devices. With the advent of sensors and features on everyday items, they have become intelligent entities able to perform multiple functions as services. IoT enables routine activities to become more intelligent, deeper communication, and processes more efficient. In the dynamic landscape of the IoT, effective service discovery is key to optimizing user experiences. A Quality of Service (QoS)-aware service discovery technique is proposed in this paper to address this challenge. Through whale optimization and genetic algorithms, our method aims to streamline decision-making processes in IoT service selection. The bio-inspired optimization techniques employed in our approach facilitate the discovery of services more efficiently than traditional methods. Our results demonstrate superior performance regarding reduced data access time, optimized energy utilization, and cost-effectiveness through comprehensive simulations.

show abstract

A Privacy-sensitive Service Selection Method Based on Artificial Fish Swarm Algorithm in the Internet of Things

Cited by 5 publications

References 13 publications

Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem

Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem

Service discovery and selection in IoT: A survey and a taxonomy

A new QoS-aware service discovery technique in the Internet of Things using whale optimization and genetic algorithms

Contact Info

Product

Resources

About