Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish

Erkennung von Menschengruppen in Bildern mit Navigationsstrategien

Eventhough the aim of the study is to recognize the human beings in the images from monocular camera without usual constraints, initially the graph theory based methods for matching are analyzed with new neighbour isomorphism. The robust Hausdorff method of matching is extended to recognize the human beings with ample models and modified distance measures. As the strategy to fuse different algorithms to get better results despite occlusions is inherently parallel, the parallel implementations on Cray T3E produced correct results in appreciably shorter computation time. As industrial applications in robotics, three example situations, one with known environment, another with unknown environment and the third with going around were discussed taking primitive models with human beings as obstacles. Unless the problem of choosing a proper model with the good indexing is available, the recognition of human beings will continue to remain as one of the hard problems to be solved.Obwohl das Ziel der vorliegenden Arbeit die Erkennung von Menschen in von einaeugigen Kameras aufgenommenen Bildern ohne die ueblichen Einschraenkungen war, wurden die urspruenglich auf Graphen basieren Matching-Verfahren mit neuen Nachbar-Isomorphie-Verfahren analysiert. Das fuer das Matching sehr zuverlaessige Hausdorff-Verfahren wurde erweitert, um Menschen anhand von vielfaeltigen Modellen und veraenderten Abstandsmessungen erkennen zu koennen. Da die Strategie der Vereinigung mehrerer Algorithmen, um trotz Okklusionen bessere Ergebnisse zu erzielen, im Grundsatz bereits parallel ist, lieferte die parallele Implementierung des Systems auf einem Cray 3TE richtige Ergebnisse in wesentlich kuerzerer Rechenzeit. Als moegliche Anwendung im Umfeld von Industrierobotern wurden drei beispielartige Situationen durchgespielt: eine in bekannter Umgebung, eine in unbekannter Umgebung und die dritte mit herumgehenden Menschen. Dazu dienten einfache Modelle mit Menschen als Hindernissen. Wenn es nicht gelingt, gute Modelle mit einer sinnvollen Indizierung zu waehlen, bleibt die computergestuetzte Erkennung von Menschen eines der nur schwer zu loesenden Probleme

An Efficient A* based Algorithm for Optimal Graph Matching applied to Computer Vision

this paper as well as the N-Queen problem will also serve the purpose of those who are not interested in optimal solution, but a quick reasonable sub-optimal solution. As these algorithms are highly parallelizable, we are proceeding now with parallelization