scholarly journals Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yongxiang Li ◽  
Xifan Yao ◽  
Min Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Service Composition ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Cloud Manufacturing ◽  
Swarm Optimization ◽  
Manufacturing Service Composition

Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

QoS–Aware Web Service Composition using Weight Improved Particle Swarm Optimization

2021 ◽  
Vol 9 (2) ◽  
pp. 65-70
Author(s):  
Laishram Jenny Chanu ◽  
◽  
Arnab Paul ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Web Services ◽  
Web Service ◽  
Optimization Algorithm ◽  
Service Composition ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Web Service Composition ◽  
Swarm Optimization ◽  
Complex Query

Lots of Web Services are available which differ in their QoS values but can perform a similar task. Discovery mechanism selects the best Web Service according to their QoS values and functional attributes. Cases arise, where the discovery mechanism fails, as a user’s complex query cannot be satisfied by a single Web Service. This can be solved by Web Service composition where multiple Web Services are combined to give a composite Web Service which meet user’s complex query. Our work is mainly focused on composition of Web Services that efficiently meets the user’s query. Different algorithms have been discussed and used by different researchers in this field. One of the most blooming topics is the use of evolutionary algorithms in optimization problems. In our work, we have chosen Particle Swarm Optimization Algorithm approach to discover the best efficient composition. Then, Weight Improved Particle Swarm Optimization Algorithm is used to improve the results which were found to be quite satisfying and efficient.

scholarly journals Research on Multiaircraft Cooperative Suppression Interference Array Based on an Improved Multiobjective Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Huan Zhang ◽  
Rennong Yang ◽  
Changyue Sun ◽  
Haiyan Han
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Minimum Width ◽  
Air Defense ◽  
Radar Network ◽  
Multiobjective Particle Swarm Optimization

For the problem of multiaircraft cooperative suppression interference array (MACSIA) against the enemy air defense radar network in electronic warfare mission planning, firstly, the concept of route planning security zone is proposed and the solution to get the minimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security zone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function, and the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization algorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to maintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation, and the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained and verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle swarm optimization algorithm for solving the problem of MACSIA is feasible and effective.

scholarly journals Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Can Liu ◽  
Zongping Li ◽  
Yueyang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Pollution ◽  
Real Time ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Path Optimization ◽  
Swarm Optimization ◽  
Real Time Traffic

In view of the lack of consideration of environmental protection performance in the traditional path optimization model, a path optimization model for urban traffic networks from the perspective of environmental pollution protection is proposed. Firstly, the urban real-time traffic condition is expressed by the road traffic state index, and an integer programming model is established to optimize the route with the goal of low carbon and shortest distribution time. Then, a hybrid particle swarm optimization algorithm combined with adaptive disturbance mechanism based on variable neighborhood descent is designed, which can better carry out adaptive disturbance according to the situation that the population falls into local extreme value, and the 2-opt local search method is introduced to improve the quality of solution. Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. The experimental demonstration of the proposed model based on two application scenarios shows that its distribution cost, distribution time, and carbon emission are 1975 yuan, 27 h, and 213 kg, respectively, which are better than other comparison models and have high application value.

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