A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy

Distributed Parallel Genetic Algorithm is the most widely a parallel genetic algorithm. It has natural parallelism and has high performance in solving complex, large-scale, non-linear, non-differentiable optimization problems. This paper analyzes the traditional limitations of distributed parallel genetic algorithms, for its migration fixed blindness and other disadvantages. A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy (AMDPGA) was proposed in this paper, which was suitable for running on the current parallel computers. This Implementation combines the Distributed Parallel Genetic Algorithm and current computer architecture, which makes the Distributed Parallel Genetic Algorithm execute on the mainstream computer concurrently and improve the convergent speed. The experiments showed that this algorithm has not only faster convergent speed but also has more accurate precision and overcome more faults as well as higher parallel efficiency.