scholarly journals Different Object Functions for SWIPT Optimization by SADDE and APSO

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1340
Author(s):  
Wei Chien ◽  
Chien-Ching Chiu ◽  
Po-Hsiang Chen ◽  
Yu-Ting Cheng ◽  
Eng Hock Lim ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Antenna Array ◽  
Millimeter Wave ◽  
Bit Error Rate ◽  
Weighting Factor ◽  
Multiple Objective ◽  
The Novel ◽  
Object Function ◽  
Swarm Optimization ◽  
Multiple Receivers

Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case.

scholarly journals NLOS Error Mitigation in Cellular Positioning using PSO Optimization Algorithm

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Stevo Lukić ◽  
Mirjana Simić
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Mobile Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Cellular Radio ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location

Non-Line-Of-Sight conditions pose a major challenge to cellular radio positioning. Such conditions, when the direct Line-Of-Sight path is blocked, result in additional propagation delay for the signal, additional attenuation, and an angular bias. Therefore,many researchers have proposed various algorithms to mitigate the measured error caused by this phenomenon. This paper presentsthe procedure for improving accuracy of determining the mobile station location in cellular radio networks in Non-Line-of-Sightpropagation environment, based on the Time Of Arrival oriented estimator using the Particle Swarm Optimization algorithm. Incomputer science, Particle Swarm Optimization is an evolutionary computational method that optimizes a problem by iteratively tryingto improve a candidate solution with regard to a given measure of quality. The proposed algorithm uses the repeating Time-Of-Arrivaltest measurements using the four base stations and for simulation selects the measurement combination that give the smallest regionenclosed by the overlap of four circles. In this way, the smallest intersect area of the four Time-Of-Arrival circles is obtained, andtherefore the smallest positioning error. After that, we consider the complete problem as a combinatorial optimization problem withthe corresponding object function that represents the nonlinear relationship between the intersection of the four circles and the mobilestation location. The Particle Swarm Optimization finds the optimal solution of the object function and efficiently determines themobile station location. The simulation results show that the proposed method outperforms conventional algorithms such as theWeighted Least Squares and the Levenberq-Marquardt method.

A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C-Means Algorithm (NIS-IFCM)

2019 ◽  
Vol 34 (05) ◽  
pp. 2055011
Author(s):  
Jing Zhao ◽  
Xiaoli Wang ◽  
Ming Li
Keyword(s):  
Computer Vision ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Classical Problem ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
Novel Method ◽  
Fuzzy C Means Algorithm ◽  
Novel Algorithm

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

Sign in / Sign up

Export Citation Format

Share Document