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Parallel multi-objective optimization using self-organized heterogeneous resources. (English) Zbl 1200.68049
Fernández de Vega, Francisco (ed.) et al., Parallel and distributed computational intelligence. Berlin: Springer (ISBN 978-3-642-10674-3/hbk; 978-3-642-10675-0/ebook). Studies in Computational Intelligence 269, 165-179 (2010).
Summary: This chapter is about using a set of parallel self-organized computing resources to perform multi-objective optimization. These computing resources are presented as a unified resource to the user where in the traditional parallel optimization paradigms the user has to assign tasks to the resources, collect the best available solutions and deal with failing resources. The main goal in this chapter is to involve the user as less as possible in the optimization process. Here the user only specifies the preferences and gives the objective functions to the system. The self-organized computing resources deliver the obtained solutions after a certain time to the user. In such a system, fast resources continue the optimization as long as the overall computing time is not over. However as the solutions of a multi-objective problem depend on each other (via the domination relation) adding a waiting time to the fast processors would affect the quality of the solutions. This has been studied on a scenario of 100 heterogeneous computing resources in the presence of failures in the system.
For the entire collection see [Zbl 1183.68011].

MSC:
68M14 Distributed systems
90C99 Mathematical programming
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