Parameter fitting of variogram based on hybrid algorithm of particle swarm and artificial fish swarm

2021 ◽  
Vol 116 ◽  
pp. 265-274
Author(s):  
Xialin Zhang ◽  
Lingkun Lian ◽  
Fukang Zhu
Keyword(s):  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Parameter Fitting ◽  
Artificial Fish Swarm

scholarly journals A New Approach to Weapon-Target Assignment in Cooperative Air Combat

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Yi-zhe Chang ◽  
Zhan-wu Li ◽  
Ying-xin Kou ◽  
Qing-peng Sun ◽  
Hai-yan Yang ◽  
...  
Keyword(s):  
Hybrid Algorithm ◽  
Harmony Search ◽  
New Approach ◽  
Artificial Fish Swarm Algorithm ◽  
Target Assignment ◽  
Air Combat ◽  
Improved Harmony Search ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

A new approach to solving weapon-target assignment (WTA) problem is proposed in this paper. Firstly, relative superiority that lays the foundation for assignment is calculated based on the combat power energy of the fighters. Based on the relative superiority, WTA problem is formulated. Afterwards, a hybrid algorithm consisting of improved artificial fish swarm algorithm (AFSA) and improved harmony search (HS) is introduced and furthermore applied to solve the assignment formulation. Finally, the proposed approach is validated by eight representative benchmark functions and two concrete cooperative air combat examples. The results show that the approach proposed in this paper achieves good performances in solving WTA problem in cooperative air combat.

scholarly journals Beam Performance Optimization of Multibeam Imaging Sonar Based on the Hybrid Algorithm of Binary Particle Swarm Optimization and Convex Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Weijie Xia ◽  
Xue Jin ◽  
Fawang Dou
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Optimization Algorithm ◽  
Performance Optimization ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Two Factors ◽  
Imaging Sonar

It should be noted that the peak sidelobe level (PSLL) significantly influences the performance of the multibeam imaging sonar. Although a great amount of work has been done to suppress the PSLL of the array, one can verify that these methods do not provide optimal results when applied to the case of multiple patterns. In order to suppress the PSLL for multibeam imaging sonar array, a hybrid algorithm of binary particle swarm optimization (BPSO) and convex optimization is proposed in this paper. In this algorithm, the PSLL of multiple patterns is taken as the optimization objective. BPSO is considered as a global optimization algorithm to determine best common elements’ positions and convex optimization is considered as a local optimization algorithm to optimize elements’ weights, which guarantees the complete match of the two factors. At last, simulations are carried out to illustrate the effectiveness of the proposed algorithm in this paper. Results show that, for a sparse semicircular array with multiple patterns, the hybrid algorithm can obtain a lower PSLL compared with existing methods and it consumes less calculation time in comparison with other hybrid algorithms.

Pigeon-inspired optimization and lateral inhibition for image matching of autonomous aerial refueling

2017 ◽  
Vol 232 (8) ◽  
pp. 1571-1583 ◽  
Author(s):  
Yongbin Sun ◽  
Haibin Duan
Keyword(s):  
Particle Swarm Optimization ◽  
Lateral Inhibition ◽  
Image Matching ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Matching Problem ◽  
Aerial Refueling ◽  
Swarm Optimization ◽  
Swarm Intelligence Algorithm ◽  
Computation Speed

Autonomous aerial refueling (AAR) is an essential application of unmanned aerial vehicles for both military and civilian domains. In this paper, a hybrid algorithm of the pigeon-inspired optimization (PIO) and lateral inhibition (LI), called LI-PIO, is proposed for image matching problem of AAR. LI is adopted for image pre-processing to enhance the edges and contrast of images. PIO, inspired from the homing characteristics of pigeons, is a novel bio-inspired swarm intelligence algorithm. To demonstrate the effectiveness and feasibility of our proposed algorithm, we make extensive comparative experiments with particle swarm optimization (PSO), particle swarm optimization based on lateral inhibition (LI-PSO), and PIO. It can be concluded from the experimental results that our proposed LI-PIO has excellent performances for image matching problem of AAR, especially in convergent rate and computation speed.

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