Based on the analysis of the properties of tent map, parallel chaos optimization algorithm (PCOA) using logistic map and outlook algorithm, a hybrid global optimization algorithm (PCOOA) is presented. The algorithm is structured in two stages. The first stage uses parallel chaos optimization based on tent map for global search, while outlook algorithm is employed in the second stage for local s...

We present results from the application of two conformational search methods genetic algorithms GA and parallel direct search methods for nding all of the low energy confor mations of a molecule that are within a certain energy of the global minimum Genetic algorithms are in a class of biologically motivated optimization methods that evolve a population of individ uals where individuals who are...

We present a parallel image clustering and search framework for large scale datasets that does not require image annotation, segmentation or registration. This work addresses the image search problem while avoiding the need for user-specified or auto-generated metadata. Instead we rely on image data alone to avoid the ambiguity inherent in user-provided information. We propose a parallel algori...

For video coding, weighing the balance between and coding rate image quality, we apply global motion search algorithm to avoid loss of image quality and parallel computing capacity of graphics processors to accelerate the encoding process. According to the heterogeneous system of CPU+GPU, and the multi-threaded parallel structure, thread synchronization features of CUDA platform, we build a pro...

Ant Colony Optimization (ACO) is recently proposed metaheuristic approach for solving hard combinatorial optimization problems. Parallel implementation of ACO can reduce the computational time obviously. An improved parallel ACO algorithm is proposed in this paper, which use dynamic transition probability to enlarge the search space by stimulating ants choosing new path at early stage; use poly...

In this paper we propose a parallel memetic algorithm which combines population-based method with guided local search (GLS) procedure. In the proposed algorithm, a GLS procedure is applied to each solution generated by genetic operations. The performance of proposed method compared with parallel genetic approaches (i.e. global model, migration model for GA). The parallel implementation is based...

We present a general picture of the parallel meta-heuristic search for optimization. We recall the main concepts and strategies in designing parallel metaheuristics, pointing to a number of contributions that instantiated them for neighborhoodand populationbased meta-heuristics, and identify trends and promising research directions. We focus on cooperation-based strategies, which display remark...

We present two sequential and one parallel global optimization codes, that belong to the stochastic class, and an interface routine that enables the use of the Merlin/MCL environment as a non-interactive local optimizer. This interface proved extremely important, since it provides flexibility, effectiveness and robustness to the local search task that is in turn employed by the global procedure...