Introduction: exploration environments In many scientific computing problems, the complexity of both the simulation and the generated data is too vast to analyze analytically or numerically. For these situations, exploration environments provide essential methods to present and explore the data in a way that allows a researcher to comprehend the information it contains. Exploration environments...

Information and communication technologies are moving towards a new stage where applications will be dynamically deployed, uninstalled, updated and (re)configured. Several approaches have been followed with the goal of creating a fully automated and context-aware deployment system. Ideally, this system should be capable of handling the dynamics of this new situation, without losing sight of oth...

Charged particle swarm optimization (CPSO) is well suited to the dynamic search problem since inter-particle repulsion maintains population diversity and good tracking can be achieved with a simple algorithm. This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity. Two types of charged swarms and an a...

In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the ...