scholarly journals A Multi-Population Genetic Algorithm for Adaptive Qos-Aware Service Composition in Fog-Iot Healthcare Environment

2021 ◽  
Author(s):  
Idir Aoudia ◽  
Saber Benharzallah ◽  
Laid Kahloul ◽  
Okba Kazar
Keyword(s):  
Genetic Algorithm ◽  
Service Composition ◽  
Execution Time ◽  
Population Genetic ◽  
Large Scale ◽  
Dynamic Change ◽  
Healthcare Environment ◽  
Fitness Value ◽  
Population Genetic Algorithm ◽  
Adaptive Qos

The growth of Internet of thing (IoT) implies the availability of a very large number of services which may be similar or the same, managing the Quality of Service (QoS) helps to differentiate one service from another.The service composition provides the ability to perform complex activities by combining the functionality of several services within a single process. Very few works have presented an adaptive service composition solution managing QoS attributes, moreover in the field of healthcare, which is one of the most difficult and delicate as it concerns the precious human life.In this paper, we will present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on multi-population genetic algorithm in Fog-IoT healthcare environment. To enhance Cloud-IoT architecture, we introduce a Fog-IoT 5-layared architecture. Secondly, we implement a QoS-Aware Multi-Population Genetic Algorithm (P-MPGA), we considered 12 QoS dimensions, i.e., Availability (A), Cost (C), Documentation (D), Location (L), Memory Resources (M), Precision (P), Reliability (R), Response time (Rt), Reputation (Rp), Security (S), Service Classification (Sc), Success rate (Sr), Throughput (T). Our P-MPGA algorithm implements a smart selection method which allows us to select the right service. Also, P-MPGA implements a monitoring system that monitors services to manage dynamic change of IoT environments. Experimental results show the excellent results of P-MPGA in terms of execution time, average fitness values and execution time / best fitness value ratio despite the increase in population. P-MPGA can quickly achieve a composite service satisfying user’s QoS needs, which makes it suitable for a large scale IoT environment.

scholarly journals Towards Service Composition based on Hybrid Bio-Inspired Cloud-based QoS Provisioning Approach.

2020 ◽  
Author(s):  
Waleed Bahgat ◽  
Mahmoud A. Salam ◽  
Ahmed Atwan ◽  
Mahmoud Badawy ◽  
Eman El-Daydamony
Keyword(s):  
Web Services ◽  
Service Composition ◽  
Execution Time ◽  
Large Scale ◽  
Performance Metrics ◽  
Solution Space ◽  
Value Added ◽  
Qos Provisioning ◽  
Fitness Value ◽  
Composition Process

Abstract It has recently become a critical issue to provide software development in a service-based conceptual style for business companies . As a powerful technology for service-oriented computing, the composition of web services is investigated. This offered great opportunities to improve IT industries and business processes by forming new value-added services that satisfy the user’s complex requirements. Unfortunately, many challenges are facing the service composition process. These include the difficulties to satisfy the user’s complex demands, maintaining the performance to be matched with the quality of service (QoS) requirements, and search space reduction for QoS missing or changeable values. Accordingly, this paper proposes a cloud-based QoS provisioning service composition (CQPC) framework to address these challenges. To prove the concept and the applicability of the CQPC framework, a Hybrid Bio-Inspired QoS provisioning (HBIQP) technique is presented for the operation of the CQPC framework modules. The solution space is reduced via utilizing skyline concepts to have faster execution time and keep only reliable and most interesting services. The CQPC framework is equipped with two proposed algorithms: (i) the modified highly accurate prediction (MHAP) algorithm to enhance the prediction of QoS values of the services participating in the composition process, (ii) the MapReduce fruit fly Particle swarm Optimization (MR-FPSO) algorithm to handle composing web services for large scale of data in the cloud environment. The experimental results demonstrate the worthiness of the HBIQP technique to meet the performance metrics more than other state-of-the-art techniques in terms of average fitness value, accuracy, and execution time.

scholarly journals Study on MPGA-BP of Gravity Dam Deformation Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyu Wang ◽  
Kan Yang ◽  
Changsong Shen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Population Genetic ◽  
Prediction Accuracy ◽  
Gravity Dam ◽  
Hydraulic Structures ◽  
Deformation Prediction ◽  
Multiple Population ◽  
Population Genetic Algorithm

Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1) falling into local minimum easily, (2) slowing convergence, and (3) the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP) neural network and multiple population genetic algorithm (MPGA). A hybrid multiple population genetic algorithm backpropagation (MPGA-BP) neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy.

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