Strategic Learning in Multiple Equilibria for Double Bargaining Mechanism by PSO

2014 ◽  
Vol 571-572 ◽  
pp. 258-261
Author(s):  
Xiao Bo Zhu
Keyword(s):  
Multiple Equilibria ◽  
Pso Algorithm ◽  
Strategic Learning ◽  
Sealed Bid ◽  
Proposed Model ◽  
Agent Simulation ◽  
Multi Agent ◽  
Learning Behaviours ◽  
Two Populations ◽  
Bargaining Mechanism

The learning behaviours of buyers and sellers with the assumption of bounded rationality were studied in the double sealed-bid bargaining mechanism. A multi-agent simulation trading system was constructed to observe the process of equilibrium approach when exist the multiple equilibria. The bidding choices of the agents were modelled by particle swarm optimization (PSO) algorithm. In our proposed model, two populations of buyers and sellers were randomly matched to deal repeatedly until the iteration stop, and each agent would update his bidding strategy in each round by imitating the successful member in his population and by private experience. Results show that the final biddings of the agents in both populations commonly approach a Nash equilibrium which is reasonable for the market principle.

Multi-Agent Simulation of Emergency Evacuation on the Sidewalk

2012 ◽  
Vol 253-255 ◽  
pp. 2005-2008
Author(s):  
Peng Chen ◽  
Shun Ying Zhu ◽  
Liang Jie Xu ◽  
Xiao Feng Ma ◽  
Zhi Gang Du
Keyword(s):  
Emergency Evacuation ◽  
Agent Based ◽  
Proposed Model ◽  
Agent Simulation ◽  
Multi Agent ◽  
Bus Station ◽  
Evacuee Behavior ◽  
Walking Time ◽  
Statistical Area

Transportation evacuation study has become a research focus in recent years. This paper deals with emergency evacuation on the sidewalk using agent-based simulation. The current study develops a traffic simulator within NetLogo, an agent-based environment. Two sub-models are proposed including facility sub-model to describe global path planning of evacuee and evacuee sub-model to describe the evacuee behavior. We conducted simulations to investigate the effect of generation position of evacuees and the proportion of choosing bus on evacuation through a case study. Simulation results indicate that the proposed model can well address the interaction among evacuees with different evacuation modes, and if evacuees choosing bus evacuate near bus station and evacuees choosing walk evacuate away from bus station, then average walking time of evacuees and maximum density in statistical area are relatively small.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

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.

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