Exemplos de uso de Particle swarm em Inglês e suas traduções para o Português
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Particle swarm optimization(PSO);
Mathematical analysis of Particle Swarm Optimization. Discrete, fuzzy PSO.
Particle swarm optimization is used to minimize the objective function that is based on robust statistics.
Used an evolutionary algorithm based on the particle swarm optimization.
The algorithm was named mpso(magnetic particle swarm optimization) and has only one configuration parameter, the number of particles. .
One of these meta-heuristic that is becoming increasingly popular is the particle swarm optimization- pso.
Optimization techniques based on particle swarm paradigm and its application to the….
The solution of the proposed optimization problem is obtained from the proposed metaheuristic named as discrete particle swarm optimization.
The optimization technique used in this work is the pso(particle swarm optimization), a method that does not use the derivatives of the ob.
We used particle swarm optimization(pso) for mesh alignment and structure from motion(sfm) for 3d reconstruction in the present method.
Two optimization methods are used in order to confirm the accuracy of the results, particle swarm and differential evolution.
Algorithms based on particle swarm optimization(pso), for example, have shown good results for this problem, but tend to converge prematurely.
The first one is a presentation of a meta-heuristic based algorithm, particle swarm optimization(pso), as a tool to solve static tnep problems.
In this paper, a particle swarm optimizer combined with a tabu list is proposed to extract the behavior patterns from time dependents phenomena.
The proposed methodologies are based on optimization algorithms bio-inspired in swarm behavior: particle swarm optimization and bat algorithm.
A website about particle swarm optimization and swarm intelligence, including introduction, bibliography, tutorials, links to online papers.
To better compare the results, two approaches were proposed:a solution based on particle swarm optimization(pso) and another based on genetic algorithm.
Two evolutionary algorithms were used for the optimization of the radiation and convection zone support systems:genetic algorithm and particle swarm optimization.
The psonet uses the particle swarm optimization technique to drive the network traffic through of a good subset of forwarders messages.
The first is an adaptation of nondominated sorting genetic algorithm- ii(nsga-ii) andthe second is a multi-objective version of the particle swarm optimization pso.
The neural oscillators were optimized with three algorithms, a genetic algorithm(ga), particle swarm optimization(pso) and nondominated sorting genetic algorithm ii nsga-ii.
In this work, three optimization methods are developed to solve the problem: proximity search(ps),iterated local search(ils) and particle swarm optimization pso.
Nowadays, the bioinspired algorithms has been contrasted,like the pso, particle swarm optimization, that is based on group intelligence, as flock of birds or school of fishes.
They will be used during the three multi-criteria optimization algorithms simulations and they are:genetic algorithm, particle swarm and cologne fireflies.
Thus, an optimization was performed through the particle swarm optimization algorithm(pso) and chaotic maps to update the efficiency parameters of this algorithm.
This research presents a method for network reconfiguration in distribution systems based on the metaheuristics¿particle swarm optimization¿.
Constitutive parameters were estimated using the particle swarm method, from cyclic experimental data obtained out of two different type of materials.
Particle swarm optimization(pso) is an artificial intelligence technique(ai) that can be used to find approximate solutions to numerical problems of maximization and minimization.
An adaptive fuzzy digital pid controller design methodology via multiobjective particle swarm optimization(mopso) based on robust stability criterion.
Particle swarm optimization is a swarm intelligence meta-heuristic which works by exchanging information between individuals from a population, aiming to optimize a specified variable.