Multi-objective Optimization using NSGA II for service composition in IoT

Kashyap, Neeti; Kumari, A. Charan; Chhikara, Rita

doi:10.1016/j.procs.2020.03.214

Cited by 33 publications

(12 citation statements)

References 12 publications

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“…The proposed method's results are compared with [36], [37], and [38] in this section and presented in different tables and figures. Moreover, two experiments are explained in the following to study the proposed method's convergence to optimal solutions and stability test.…”

Section: A the Simulation Results' Studymentioning

confidence: 99%

“…In the first experiment, the resulting data of [36] are used to evaluate the proposed method in this thesis. The proposed method in [36] is based on a multi-objective genetic algorithm to investigate the service composition of IoT that is evaluated with the different number of tasks and candidate services for the tasks. Hence, our proposed method is evaluated in this experiment with ten tasks and 10, 30, and 50 candidate services.…”

Section: A the Simulation Results' Studymentioning

confidence: 99%

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Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Wang¹,

Liu²

2023

IJACSA

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Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and looselycoupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.

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Section: A the Simulation Results' Studymentioning

confidence: 99%

Section: A the Simulation Results' Studymentioning

confidence: 99%

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Wang¹,

Liu²

2023

IJACSA

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“…Kashyap et al [16] use the NSGA2 multi-objective metaheuristic search method to find the best solution to the Service Composition Problem (SCP) in the context of Internet of Things.…”

Section: Related Workmentioning

confidence: 99%

DDAPSO: Hybrid Discrete Dragonfly Algorithm and Particle Swarm Algorithm to Service Selection and Composition for the Internet of Things Applications

Benmessahel¹,

Nouioua²

2022

RIA

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Recently, the Internet of Things (IoT) has quickly risen as one of the most essential technologies of this century. IoT allows users to connect to a vast network of smart devices, services, and data. An important and challenging research problem in the Internet of Things applications is how to select an appropriate service selection (SS). In the SS problem, users can combine several services from diverse sources (things or devices) to satisfy their needs. On the other hand, the SS problem is known for its complexity and is categorized as an NP-hard problem; such problems are typically solved utilizing heuristics like bio-inspired algorithms. In this research a new bio-inspired algorithm called DDAPSO is created to solve the SS problem where a new strategy is proposed to maintain a balance between the exploration and exploitation abilities. This hybrid algorithm is the result of coupling a Discrete Dragonfly Algorithm (DDA) with the particle swarm optimization algorithm (PSO). The suggested algorithm was properly tested using a variety of scenarios with different numbers of services and with different numbers of concrete services per each service set or task. The proposed algorithm is compared with the main recent well-known algorithms, i.e. GA, PSO, DDA, ABC and MVO for service selection. In a large-scale setting, the results clearly show that the DDAPSO algorithm outperforms other services selection algorithms reported in the literature in terms of selection optimally as well as execution time.

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“…Lower operational and material costs in supply chain operations invariably increase the enterprise's pro t margins and nancial performance [17]. Activities, elements, and procedures in the supply chain once optimized, shall result in rm performance and e ciency, which in turn, refer to revenue generation, and pro tability [15]. There must be a complete integration among the entities so that information of the supply chain transactions can be shared in real-time always in order to meet the highly uctuating demand of the customers.…”

Section: Supply Chain Operations -Revenue Generationmentioning

confidence: 99%

Optimization of End-to-end Supply Chain Costs to Maximize Revenue and Profits From Supply Chain Operations

Damodaram¹,

Reddy²,

Davanam³

et al. 2022

Preprint

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Being aware that the supply chain costs claim a substantial share in the final product cost, organizations, in an effort to improve revenue and increase profitability, should minimize supply chain costs invariably. Besides other costs, supply chain costs vary from time to time depending on the sourcing, transportation, and inventory-related costs. Improving the operational efficiency of the supply chain has become the primary objective for increasing profitability from supply chain operations in present-day context. In reality, supply chain operations are complex with uncertainty predominantly influencing various costs associated with supply chains affecting return on investments and profitability. Supply chain cost optimization should enable the supply chain managers to ensure that the supply chain is ‘responsive’, ‘agile’, and ‘reinvented’ for present and future operations. Optimization of supply chain costs bestows improving operating efficiency that promotes improvements in revenue and increment in profits. The present article aims at considering various supply chains cost parameters such as total operating costs, profit, revenue, logistics costs, and information sharing costs for optimization of supply chain costs using multi-objective optimization. A generic three-stage supply chain model with usual entities like suppliers, manufacturers, and customer zones is assumed in the present article. A trade-off curve among the selected objective function sets and relevant constraints was developed and presented in the article. The trade-off curve provides the basis for decision-makers to better understand the supply chain optimization effect on the selected objective function set elements to make the present and future supply chains promote startling ‘profitability’ and increase revenue generation in operations.

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Multi-objective Optimization using NSGA II for service composition in IoT

Cited by 33 publications

References 12 publications

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

DDAPSO: Hybrid Discrete Dragonfly Algorithm and Particle Swarm Algorithm to Service Selection and Composition for the Internet of Things Applications

Optimization of End-to-end Supply Chain Costs to Maximize Revenue and Profits From Supply Chain Operations

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