99 research outputs found
An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes
Quantum-enhanced metrology infers an unknown quantity with accuracy beyond
the standard quantum limit (SQL). Feedback-based metrological techniques are
promising for beating the SQL but devising the feedback procedures is difficult
and inefficient. Here we introduce an efficient self-learning
swarm-intelligence algorithm for devising feedback-based quantum metrological
procedures. Our algorithm can be trained with simulated or real-world trials
and accommodates experimental imperfections, losses, and decoherence
State Transition Algorithm
In terms of the concepts of state and state transition, a new heuristic
random search algorithm named state transition algorithm is proposed. For
continuous function optimization problems, four special transformation
operators called rotation, translation, expansion and axesion are designed.
Adjusting measures of the transformations are mainly studied to keep the
balance of exploration and exploitation. Convergence analysis is also discussed
about the algorithm based on random search theory. In the meanwhile, to
strengthen the search ability in high dimensional space, communication strategy
is introduced into the basic algorithm and intermittent exchange is presented
to prevent premature convergence. Finally, experiments are carried out for the
algorithms. With 10 common benchmark unconstrained continuous functions used to
test the performance, the results show that state transition algorithms are
promising algorithms due to their good global search capability and convergence
property when compared with some popular algorithms.Comment: 18 pages, 28 figure
Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization
We study the evolution of cooperation among selfish individuals in the
stochastic strategy spatial prisoner's dilemma game. We equip players with the
particle swarm optimization technique, and find that it may lead to highly
cooperative states even if the temptations to defect are strong. The concept of
particle swarm optimization was originally introduced within a simple model of
social dynamics that can describe the formation of a swarm, i.e., analogous to
a swarm of bees searching for a food source. Essentially, particle swarm
optimization foresees changes in the velocity profile of each player, such that
the best locations are targeted and eventually occupied. In our case, each
player keeps track of the highest payoff attained within a local topological
neighborhood and its individual highest payoff. Thus, players make use of their
own memory that keeps score of the most profitable strategy in previous
actions, as well as use of the knowledge gained by the swarm as a whole, to
find the best available strategy for themselves and the society. Following
extensive simulations of this setup, we find a significant increase in the
level of cooperation for a wide range of parameters, and also a full resolution
of the prisoner's dilemma. We also demonstrate extreme efficiency of the
optimization algorithm when dealing with environments that strongly favor the
proliferation of defection, which in turn suggests that swarming could be an
important phenomenon by means of which cooperation can be sustained even under
highly unfavorable conditions. We thus present an alternative way of
understanding the evolution of cooperative behavior and its ubiquitous presence
in nature, and we hope that this study will be inspirational for future efforts
aimed in this direction.Comment: 12 pages, 4 figures; accepted for publication in PLoS ON
Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems
This paper presents the first investigation on applying a particle swarm optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (Proc. of the 7th Metaheuristics International Conference, 2007), and also propose two novel local search heuristics within our JPSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our JPSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed JPSO algorithm over a number of other state-of-the-art approaches
Optimalni neizraziti reglutor tipa 2 za sustave za grijanje, ventilaciju i klimatizaciju
In this paper a novel Optimal Type-2 Fuzzy Proportional-Integral-Derivative Controller (OT2FPIDC) is designed for controlling the air supply pressure of Heating, Ventilation and Air-Conditioning (HVAC) system. The parameters of input and output membership functions, and PID controller coefficients are optimized simultaneously by random inertia weight Particle Swarm Optimization (RNW-PSO). Simulation results show the superiority of the proposed controller than similar non-optimal fuzzy controller.U radu je predloĆŸena nova upravljaÄka shema optimalnog neizrazitog PID regulatora tipa 2 za upravljanje sustavima za grijajne, ventilaciju i klimatizaciju. PredloĆŸena je shema zasnovana na neizrazitom regulatoru (FLC) uÄestalo koriĆĄtenom za upravljajne nelinearnim procesima. Kako bi se premostio problem neizrazitih regulatora, neodstatak metode dizajnirajna, parametri ulazno-izlaznih funkcija pripadanja, kao i parametri PID regulatora se optimiraju metodom roja Äestica sa sluÄajnim parametrima inercije (RNW-PSO). Simulacijski rezultati pokazuju izvedivost predloĆŸenog pristupa
International Consensus Statement on Rhinology and Allergy: Rhinosinusitis
Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICARâRS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICARâRSâ2021 as well as updates to the original 140 topics. This executive summary consolidates the evidenceâbased findings of the document. Methods: ICARâRS presents over 180 topics in the forms of evidenceâbased reviews with recommendations (EBRRs), evidenceâbased reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICARâRSâ2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidenceâbased management algorithm is provided. Conclusion: This ICARâRSâ2021 executive summary provides a compilation of the evidenceâbased recommendations for medical and surgical treatment of the most common forms of RS
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