In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimization, Differential Evolution, and Scatter Search. A GPU-based implementation, obviously, does not change the general properties of the algorithms. As well, we give for granted that GPU-based implementation of both algorithm and fitness function produces a significant speed-up with respect to a sequential implementation. Accordingly, the main goal of this work has been to fairly assess the efficiency of the GPU-based implementations of the three metaheuristics, based on the statistical analysis of the results they obtain in optimizing a benchmark of twenty functions within a prefixed limited time. © 2012 Springer-Verlag.SCOPUS: cp.kinfo:eu-repo...
Inspired by the collective behavior of natural swarm, swarm intelligence algorithms (SIAs) have been...
The paper presents the results of comparison of three metaheuristics that currently exist in the pro...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimizat...
Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly...
In this paper, a GPU-based implementation of Differential Evolution (DE) and Particle Swarm Optimiza...
Intelligent optimization algorithms are very effective to tackle complex problems that would be diff...
Abstract—Intelligent optimization algorithms are very effec-tive to tackle complex problems that wou...
Metaheuristics have been showing interesting results in solving hard optimization problems. However,...
Abstract. We consider a metaheuristic optimization algorithm which uses single process (thread) to g...
Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last...
This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Opti...
Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically par...
Hybrid metaheuristics are powerful methods for solving com- plex problems in science and industry. N...
Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically par...
Inspired by the collective behavior of natural swarm, swarm intelligence algorithms (SIAs) have been...
The paper presents the results of comparison of three metaheuristics that currently exist in the pro...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...
In this paper we compare GPU-based implementations of three metaheuristics: Particle Swarm Optimizat...
Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly...
In this paper, a GPU-based implementation of Differential Evolution (DE) and Particle Swarm Optimiza...
Intelligent optimization algorithms are very effective to tackle complex problems that would be diff...
Abstract—Intelligent optimization algorithms are very effec-tive to tackle complex problems that wou...
Metaheuristics have been showing interesting results in solving hard optimization problems. However,...
Abstract. We consider a metaheuristic optimization algorithm which uses single process (thread) to g...
Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last...
This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Opti...
Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically par...
Hybrid metaheuristics are powerful methods for solving com- plex problems in science and industry. N...
Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically par...
Inspired by the collective behavior of natural swarm, swarm intelligence algorithms (SIAs) have been...
The paper presents the results of comparison of three metaheuristics that currently exist in the pro...
Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the ef...