Adaptive Load Balancing in the Modified Mind Evolutionary Computation Algorithm

Maxim K. Sakharov, Anatoly P. Karpenko


The paper presents an adaptive load balancing method for the modified parallel Mind Evolutionary Computation (MEC) algorithm. The proposed method takes into account an objective function's topology utilizing the information obtained during the landscape analysis stage as well as the information on available computational resources. The modified MEC algorithm and proposed static load balancing method are designed for loosely coupled parallel computing systems and imply a minimal number of interactions between computational nodes when solving global optimization problems. A description of the proposed method is presented in this work along with the results of computational experiments, which were carried out with a use of multi-dimensional benchmark functions of various classes. Obtained results demonstrate that an effective use of available computational resources in the proposed method helps finding a better solution comparing to the traditional parallel MEC algorithm balancing. Further development of the proposed method requires more advanced termination criteria in order to avoid excessive iterations.

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