Design optimization of long period waveguide grating devices for refractive index sensing using adaptive particle swarm optimization

2016 ◽  
Vol 359 ◽  
pp. 336-343 ◽  
Author(s):  
Girish Semwal ◽  
Vipul Rastogi
Keyword(s):  
Refractive Index ◽  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Waveguide Grating ◽  
Adaptive Particle Swarm Optimization ◽  
Long Period ◽  
Refractive Index Sensing

Adaptive Particle Swarm Optimization with Dynamic Population and its Application to Constrained Engineering Design Optimization

2012 ◽  
Vol 538-541 ◽  
pp. 3074-3078
Author(s):  
Yi Liu ◽  
Cai Hong Mu ◽  
Wei Dong Kou ◽  
Jing Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Population Size ◽  
Engineering Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Engineering Design Optimization ◽  
Dynamic Population

This paper presents a variant of the particle swarm optimization (PSO) that we call the adaptive particle swarm optimization with dynamic population (DP-APSO), which adopts a novel dynamic population (DP) strategy whereby the population size of swarm can vary with the evolutionary process. The DP strategy enables the population size to increase when the swarm converges and decrease when the swarm disperses. Experiments were conducted on two well-studied constrained engineering design optimization problems. The results demonstrate better performance of the DP-APSO in solving these engineering design optimization problems when compared with two other evolutionary computation algorithms.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

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