Optimizing QoS-Based Web Services Composition by Using Quantum Inspired Cuckoo Search Algorithm

Author(s):  
Serial Rayene Boussalia ◽  
Allaoua Chaoui
2012 ◽  
Vol 532-533 ◽  
pp. 1836-1840 ◽  
Author(s):  
Yan Yan Zhang ◽  
Hai Ling Xiong ◽  
Yong Chun Zhang

A web service composition method based on the adaptive genetic operator was proposed to deal with the issues of the lack of adaptability and the easy-premature phenomena in web services composition genetic algorithm. Adaptive crossover and mutation operator were designed according to the individual adaptability and evolution stage for enlarging local search range and increasing convergent speed. Moreover, use for reference the idea of taboo table in taboo search algorithm, we can inhibit the algorithm from converging to false optimal solution untimely; meanwhile, an evolution strategy was adopted to prevent the loss of composite service with high fitness value. The experimental result shows that better composite services can be gotten through the improved algorithm; moreover the convergence speed has also been improved.


2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.


2011 ◽  
Vol 22 (11) ◽  
pp. 2698-2715 ◽  
Author(s):  
Fang-Xiong XIAO ◽  
Zhi-Qiu HUANG ◽  
Zi-Ning CAO ◽  
Li-Zhong TU ◽  
Yi ZHU

Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.


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