<p>The OP stands for all kinds of application problems, which can be computationally modeled as an optimization problem. Three such models are possible for solving OP, namely , and , where and are derived from how to solve OP numerically and non-numerically respectively. Three parallel solutions can be applied to solve the modeled optimization problems, and the current parallel platform at both hardware and software level can easily support the above three solutions.</p
This thesis is devoted to the study of metaheuristic optimization algorithms and their application i...
Many programming systems available on massively parallel processors (MPPs) today neglect the signifi...
Many optimization problems are generally complex and required to be solved in parallel architectures...
Meta-heuristic PSO has limits, such as premature convergence and high running time, especially for c...
The increasing exploration of alternative methods for solving optimization problems causes that para...
The increasing computational cost in complex optimization problems that have a large size resulted i...
PSO is a population based evolutionary algorithm and is motivated from the simulation of social beha...
In the perspective of parallel processing, a new sense of parametric optimization might be promoted....
The paper presents an analysis of the use of optimization algorithms in parallel solutions and distr...
Abstract. We consider a metaheuristic optimization algorithm which uses single process (thread) to g...
We propose a new distributed and parallel meta-heuristic framework to address the issues of scalabil...
International audienceFor a couple of years, all processors in modern machines are multi-core. Massi...
This work deals with particle swarm optimization. The theoretic part briefly describes the problem o...
Abstract—In optimization problems involving large amounts of data, such as web content, commercial t...
this paper is to show how the search algorithm known as particle swarm optimization performs. Here,...
This thesis is devoted to the study of metaheuristic optimization algorithms and their application i...
Many programming systems available on massively parallel processors (MPPs) today neglect the signifi...
Many optimization problems are generally complex and required to be solved in parallel architectures...
Meta-heuristic PSO has limits, such as premature convergence and high running time, especially for c...
The increasing exploration of alternative methods for solving optimization problems causes that para...
The increasing computational cost in complex optimization problems that have a large size resulted i...
PSO is a population based evolutionary algorithm and is motivated from the simulation of social beha...
In the perspective of parallel processing, a new sense of parametric optimization might be promoted....
The paper presents an analysis of the use of optimization algorithms in parallel solutions and distr...
Abstract. We consider a metaheuristic optimization algorithm which uses single process (thread) to g...
We propose a new distributed and parallel meta-heuristic framework to address the issues of scalabil...
International audienceFor a couple of years, all processors in modern machines are multi-core. Massi...
This work deals with particle swarm optimization. The theoretic part briefly describes the problem o...
Abstract—In optimization problems involving large amounts of data, such as web content, commercial t...
this paper is to show how the search algorithm known as particle swarm optimization performs. Here,...
This thesis is devoted to the study of metaheuristic optimization algorithms and their application i...
Many programming systems available on massively parallel processors (MPPs) today neglect the signifi...
Many optimization problems are generally complex and required to be solved in parallel architectures...