SCALABILITY OF BIO-HEURISTICS FOR MULTIDIMENSIONAL OPTIMIZATION PROBLEMS

Abstract

A scalable bio-heuristic algorithm capable of solving multidimensional optimization problems is proposed. Special operators are used to support the diversity of the solution population, to expand the search area for solutions at the expense of less promising solutions. The efficiency of the proposed algorithm is evaluated on a set of multidimensional functions of Grivank, Rastrigin, Rosenbrock, and Schwefel. The indicators of the developed algorithm are com-pared with those of competing algorithms.