###### Abstract
In order to avoid network paralysis caused by external malicious attacks or internal failures as much as possible, effective measures have to be taken to protect data from being leaked. The network security is enhanced by reasonably deploying the controller on nodes with high-security performance. To sum up, different multi-objective functions can be selected to optimize the placement of the controller according to the requirements of different network scenarios. A reasonable controller placement model has to be established in the case of simultaneous optimization of multiple objectives. Two sub-problems should be solved:  
1. Determine the number of controllers. 2. Determine the placement position of the controllers and which controller the switch should be assigned to. Based on analysis of the problem, the controller placement problem is an NP-Hard problem [16], and the use of a heuristic algorithm is an effective method. Therefore, for the distributed controller placement of particular network scenarios, this paper proposes a heuristic-based multi-controller placement approach called MODECP. Firstly, multiple objective models are established, including the network end-to-end delay, network security, controller load rate, and link cost. In particular, the network end-to-end delay is expressed as switch-controller propagation delay, inter-controller propagation delay, processing delay, and transmission delay. The network security is modeled as the failure cost of the controller based on degree centrality, and the failure cost of the control link is based on betweenness centrality. Meanwhile, the link cost is modeled as the link bandwidth occupied by switch-controller communication and inter-controller synchronization. Secondly, an improved approach based on affinity propagation algorithm is proposed, which adaptively obtains a reasonable and effective number of controllers. Finally, we propose an improved multi-objective differential evolution algorithm that solves the controller placement problem and switches assignment to improve network performance efficiently. The main contributions of this paper are as follows:  
1. The controller placement problem is effectively solved, and the network delay, network security, controller load rate, and link cost are modeled. At the same time,a multi-objective model to solve the controller placement problem is obtained by weighing multiple objectives. 2. On the one hand, an improved affinity propagation algorithm is proposed, which integrates the characteristics of network nodes, including delay, switch security, and load. On the other hand, a controller placement approach based on a multi-objective differential evolution algorithm is proposed to optimize performance objectives such as delay, security, load balancing, and link cost. 3. The proposed method is simulated extensively. We compare it with various single-objective based schemes. In addition, we compare it to the multi-objective based genetic algorithm and the delay-based random placement algorithm. Furthermore, we compare the proposed approach under different topologies. The feasibility and efficiency of MODECP are verified by comparing the performance. In addition, to better evaluate the impact of delay and link state changes on the deployment results, we change the delay calculation parameters and the link failure states predicted using the SVM model to represent the performance of MODECP. The structure of this paper is as follows. Section 2 briefly discusses related work, and Section 3 describes the system model and problem formulation of distributed controller placement. Section 4 proposes a MODECP approach based on the improved affinity propagation algorithm and multi-objective differential evolution algorithm. The proposed algorithm is simulated, and the performance is analyzed and evaluated in Section 5. Section 6 summarizes this paper.