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We present a new concurrency control abstraction for real-time systems called preemptible atomic regions (PARs). PARs a transactional mechanism that improves upon lock-based mutual exclusion in several ways. First, and foremost, PARs provide strong correctness guarantees. Any sequence of operations declared atomic will not suffer interference from other(More)
This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical(More)
—Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and convergence speed of optimization algorithms. In this paper, definitions of(More)
Feature-wise decomposition is an important approach to building configurable software systems. Although there has been research on the usefulness of particular tools for feature-wise decomposition, there are not many informative comparisons on the relative effectiveness of different tools. In this paper, we compare AspectJ and Jiazzi, which are two(More)
Traditional dynamic program slicing techniques are code-centric, meaning dependences are introduced between executed statement instances, which gives rise to various problems such as space requirement is decided by execution length; dependence graphs are highly redundant so that inspecting them is labor intensive. In this paper, we propose a data-centric(More)
—Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimiz-ers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers,(More)
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics——differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its(More)
As a population-based optimizer, the differential evolution (DE) algorithm has a very good reputation for its competence in global search and numerical robustness. In view of the fact that each member of the population is evaluated individually, DE can be easily parallelized in a distributed way. This paper proposes a novel distributed memetic differential(More)