Flexible Fuzzy Rule Bases Evolution with Swarm Intelligence for Meta-Scheduling in Grid Computing

Fuzzy rule-based systems are expert systems whose performance is strongly related to the quality of their knowledge and the associated knowledge acquisition processes and thus, the design of effective learning techniques is considered a critical and major problem of these systems. Knowledge acquisition with a swarm intelligence approach is a recent learning strategy for the evolution of fuzzy rule bases founded on swarm intelligence showing improvement over classical knowledge acquisition strategies in fuzzy rule based systems such as Pittsburgh and Michigan approaches in terms of convergence behaviour and accuracy. In this work, a generalization of this method is proposed to allow the simultaneous consideration of diversely configured knowledge bases and this way to accelerate the learning process of the original algorithm. In order to test the suggested strategy, a problem of practical importance nowadays, the design of expert meta-schedulers systems for grid computing is considered. Simulations results show the fact that the suggested adaptation improves the functionality of knowledge acquisition with a swarm intelligence approach and it reduces computational effort; at the same time it keeps the quality of the canonical strategy

The Trilogy of Science: Filling the Knowledge Management Gap with Knowledge Science and Theory

The international knowledge management field has different ways of investigating, developing, believing, and studying knowledge management. Knowledge management (KM) is distinguished deductively by know-how, and its intangible nature establishes different approaches to KM concepts, practices, and developments. Exploratory research and theoretical principles have formed functional intelligences from 1896 to 2013, leading to a knowledge management knowledge science (KMKS) concept that derived a grounded theory of knowledge activity (KAT). This study addressed the impact of knowledge production problems on KM practice. The purpose of this qualitative meta-analysis study was to fit KM practice within the framework of knowledge science (KS) study. Themed questions and research variables focused on field mechanisms, operative functions, principle theory, and relationships of KMKS. The action research used by American practitioners has not established a formal structure for KS. The meta-data-analysis examined 385 transdisciplinary peer-reviewed articles using social science, service science, and systems science databases, with a selection of interdisciplinary studies that had a practice-research-theory framework. Key attributes utilizing Boolean limiters, words, phrases and publication dates, along with triangulation, language analysis and coding through analytic software identified commonalities of the data under study. Findings reflect that KM has not become a theoretically saturated field. KS as the forensic science of KM creates a paradigm shift, causes social change that averts rapid shifts in management direction and uncertainty, and connects KM philosophy and science of knowledge. These findings have social change implications by informing the work of managers and academics to generate a methodical applied science