Parallelization strategies for evolutionary algorithms for MINLP

Two different parallelization strategies for evolutionary algorithms for mixed integer nonlinear programming (MINLP) are discussed and numerically compared in this contribution. The first strategy is to parallelize some internal parts of the evolutionary algorithm. The second strategy is to parallelize the MINLP function calls outside and independently of the evolutionary algorithm. The first strategy is represented here by a genetic algorithm (arGA) for numerical testing. The second strategy is represented by an ant colony optimization algorithm (MIDACO) for numerical testing. It can be shown that the first parallelization strategy represented by arGA is inferior to the serial version of MIDACO, even though if massive parallelization via GPGPU is used. In contrast to this, theoretical and practical tests demonstrate that the parallelization strategy of MIDACO is promising for cpu-time expensive MINLP problems, which often arise in real world applications.

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