Probability-based genetic algorithm for solving complex multiobjective optimization tasks using adaptive mutation operator and Pareto set prediction

Abstract


The article is devoted to a new algorithm for solving complex constrained multiobjective optimization tasks on the basis of probability-based GA. An efficient modification of the algorithm using adaptive mutation and Pareto set prediction is proposed. The results of efficiency investigation are presented.


About the authors

Ye. A. Sopov

Siberian State Aerospace University

Author for correspondence.
Email: es_gt@mail.ru

Russian Federation

candidate of technical sciences, associate professor

S. A. Sopov

Siberian State Aerospace University

Email: sopov_sergey@mail.ru

Russian Federation

student

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