Mind evolutionary computation with swarm intelligence

Abstract

Mind evolutionary computation (MEC) is a novel stochastic algorithm that derived from man's swarm intelligence. Based on the swarm theory, the social behavior analysis about MEC is made and the searching mechanisms of its operators are studied. After that, a parameter analysis is provided for the similar axis operator, and a cooperation-based dissimilation operator (CDO) is developed. Finally, a series of experiments have been done to make a parameter choice and an evaluation for MEC. The results illustrate MEC with CDO is a viable global optimization method owning robust ability.

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