Particle Swarm Optimization
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Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2280
Nafees Ul Hassan ◽  
Waqas Haider Bangyal ◽  
M. Sadiq Ali Khan ◽  
Kashif Nisar ◽  
Ag. Asri Ag. Ibrahim ◽  

Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence.

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Lulu Liu

In recent years, disasters have seriously affected the normal development of financial business in some regions. At the time of disaster, how to effectively integrate resources of all parties, deal with sudden financial disasters efficiently, and restore financial services in time has become an important task. Therefore, this paper adopts Particle Swarm Optimization (PSO) to improve the traditional BP Neural Network (BPNN) and finally constructs a Particle Swarm Optimization powered BP Neural Network (PSO-BPNN) model for the intelligent emergency risk avoidance of sudden financial disasters in digital economy. At the same time, the proposed algorithm is also compared to GA-BPNN and BPNN algorithms, which are also intelligent algorithms. Experimental results show that the hybrid PSO-BPNN algorithm is superior to GA-BPNN algorithm and BPNN algorithm in simulation and prediction effect. It can accurately predict the sudden financial disaster in recent period, so the model has a good application prospect.

Shipeng Duan ◽  
Zengxiang Zhou ◽  
Jiale Zuo ◽  
Mengtao Li ◽  
Zhigang Liu ◽  

Abstract To date, the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) has been in operation for 12 years. To improve the telescope’s astronomical observation accuracy, the original open-loop fibre positioning system of LAMOST is in urgent need of upgrading. The upgrade plan is to locate several fibre view cameras (FVCs) around primary mirror B to build a closed-loop feedback control system. The FVCs are ~20 m from the focal surface. To reduce a series of errors when the cameras detect the positions of the optical fibres, we designed fiducial fibres on the focal surface to be fiducial points for the cameras. Increasing the number of fiducial fibres can improve the detection accuracy of the FVC system, but it will also certainly reduce the number of fibre positioners that can be used for observation. Therefore, the focus of this paper is how to achieve the quantity and distribution that meet the requirements of system detection. In this paper, we introduce the necessity of using fiducial fibres, propose a method for selecting their number, and present several methods for assessing the uniformity of their distribution. Finally, we use particle swarm optimization to find the best distribution of fiducial fibres.

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