The most important aspect in IoT is security. The provision of security in IoT systems is the responsibility of a trust management mechanism. However a trust management mechanism comprises a number of components, of which secure routing is vital among them. There are a number of effective parameters in secure routing which have been considered in the presented multi-objective optimization model. In this paper, MultiObjective Learning Automata (MOLA) was used to solve secure routing problem which can simultaneously optimize all parameters. There exist three methods of training LA, and the results of the different methods were compared in this study. The proposed approach can be used with both administrator and users because their requirements are considered in a model and it is quite easy for administrator and users to comprehend.

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Security management is a challengeable concept of computer system in distributed environment. Classical approaches do not adapt to circumstances, so developers can not design a computer system base on common approaches that be controlled with administrator. In this paper, a layering approach is presented for security management. Proposed approach clusters the users, so administrator can control relation for security. Proposed approach enriches with multi-objective evolutionary optimization algorithm. Multi-objective evolutionary optimization algorithms have dynamic process, so they can adapt to conditions of distributed environment.