Dynamic multi-objective evolutionary algorithm for IoT services

The primary goal of the Internet of things(IoT) is to provide people with anywhere services in real life. But intelligent IoT shouldn’t only provide services, but also consider how to allocate heterogeneous resources reasonably, which has become a very challenging problem. To obtain the best resourc...

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Vydáno v:Applied intelligence (Dordrecht, Netherlands) Ročník 51; číslo 3; s. 1177 - 1200
Hlavní autoři: Fang, Shun-shun, Chai, Zheng-yi, Li, Ya-lun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2021
Springer Nature B.V
Témata:
Artificial Intelligence
Computer Science
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Internet of Things
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Optimization
Polynomials
Processes
Resource allocation
Differential evolution
Internet of things
Agriculture
Dynamic multi-objective optimization
Multi-objective evolutionary algorithm
ISSN:0924-669X, 1573-7497
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Shrnutí:The primary goal of the Internet of things(IoT) is to provide people with anywhere services in real life. But intelligent IoT shouldn’t only provide services, but also consider how to allocate heterogeneous resources reasonably, which has become a very challenging problem. To obtain the best resource allocation scheme, it is crucial to minimize the service cost and service time. Since the two objectives are contradictory, we have modelled IoT services as a dynamic multi-objective optimization problem. Then a dynamic multi-objective evolutionary algorithm for dynamic IoT services(dMOEA/DI) is proposed. In dMOEA/DI, we have designed operators such as the appropriate encoding method, dynamic detection operator, filtering strategy, differential evolution, and polynomial mutation. Based on the single service strategy and collaborative service strategy, experimental research is performed on the agricultural IoT services with dynamic requests under different distributions. The simulation experimental results prove that dMOEA/DI performs better than the contrasted algorithms on the IoT service optimization problems.
Bibliografie:ObjectType-Article-1
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-01861-7