BEECLUST: A Swarm Algorithm Derived from Honeybees: Derivation of the Algorithm, Analysis by Mathematical Models, and Implementation on a Robot Swarm

2016 ◽  
pp. 113-156 ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 4558-4567
Author(s):  
Wenwen Deng

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.


Author(s):  
Wenzhong Wang ◽  
Shusheng Zhang ◽  
Suihuai Yu

Based on PSO-BP algorithm combining particle swarm algorithm with BP neural network algorithm, this paper applies this algorithm to image restoration based on optimization. In the PSO-BP optimization algorithm model, on the one hand, the error of each training sample of BP algorithm is reversed, and the original image is used as the reference to modify the weight threshold of BP algorithm. On the other hand, it is optimized by forward particle swarm algorithm and BP algorithm. Finally, through the algorithm analysis and experimental data, the recovery effect of PSO-BP optimization algorithm is better than that of the same type algorithm.


Author(s):  
Giulia De Masi ◽  
Judhi Prasetyo ◽  
Raina Zakir ◽  
Nikita Mankovskii ◽  
Eliseo Ferrante ◽  
...  

AbstractIn this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.


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