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This study proposes a novel design and implementation of Differential Evolution (DE) using the Partitioned Global Address Space (PGAS) parallel computing model and the Unified Parallel C (UPC) programming language. The mapping of DE concepts to UPC features is presented and a DE useful for both many-core shared memory systems and clusters of computers with distributed memory is implemented and evaluated...
Differential evolution is an efficient populational meta-heuristic optimization algorithm that has been applied to many difficult real world problems. Due to the relative simplicity of its operations and real encoded data structures, it is suitable for a parallel implementation on multicore systems and on Graphic Processing Units (GPUs) that nowadays reach peak performance of hundreds and thousands...
Recently, the interests of solving large-scale optimization problems have increased in the field of evolutionary algorithms. This paper presents a novel differential evolution, namely EOBDE, to solve these kinds of problems by using elite opposition-based learning strategy. In the proposed algorithm, the opposite solutions of some selected elite individuals from the current population are generated...
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