Abstract — Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimization, and in many cases enables a faster convergence to the ideal solution. However, like any optimization algorithm it seems to have difficulties handling optimization problems of high dimension. Here we first show that dimensionality is really a problem for the classical particle swarm algorithms. We then show that increasing the swarm size can be necessary to handle problem of high dimensions but is not enough. We also show that the issue of scalability occurs more quickly on some functions. I
Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms...
The particle swarm optimization (PSO) was introduced as a population based stochastic search and opt...
In the past two decades, different kinds of nature-inspired optimization algorithms have been design...
Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimiz...
This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm...
We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for th...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have...
Particle swarm optimisation (PSO) is a stochastic, population-based optimisation algorithm. PSO has ...
The optimization of high-dimensional functions is an important problem in both science and engineeri...
Large scale continuous optimization problems are more relevant in current benchmarks since they are ...
In this paper we provide a rigorous convergence analysis for the renowned particle swarm optimizatio...
This paper illustrates the importance of independent, component-wise stochastic scaling values, from...
This paper attempts to address the question of scaling up Particle Swarm Optimization (PSO) algorith...
Particle swarm optimization (PSO) is a simple metaheuristic method to implement with robust performa...
The same mechanisms that are so efficient at finding optima may result in a conventional Particle Sw...
Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms...
The particle swarm optimization (PSO) was introduced as a population based stochastic search and opt...
In the past two decades, different kinds of nature-inspired optimization algorithms have been design...
Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimiz...
This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm...
We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for th...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have...
Particle swarm optimisation (PSO) is a stochastic, population-based optimisation algorithm. PSO has ...
The optimization of high-dimensional functions is an important problem in both science and engineeri...
Large scale continuous optimization problems are more relevant in current benchmarks since they are ...
In this paper we provide a rigorous convergence analysis for the renowned particle swarm optimizatio...
This paper illustrates the importance of independent, component-wise stochastic scaling values, from...
This paper attempts to address the question of scaling up Particle Swarm Optimization (PSO) algorith...
Particle swarm optimization (PSO) is a simple metaheuristic method to implement with robust performa...
The same mechanisms that are so efficient at finding optima may result in a conventional Particle Sw...
Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms...
The particle swarm optimization (PSO) was introduced as a population based stochastic search and opt...
In the past two decades, different kinds of nature-inspired optimization algorithms have been design...