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It is axiomatic that influence maximization is one of the major issues of the Internet today. In this paper a novel specialized metaheuristic algorithm is proposed to efficiently deal with it. The purpose of influence maximization is to select a subset of seed nodes in the network in order to influence other nodes maximally. The greedy methods present good results for influence maximization, but these algorithms involve very high computational time. Unlike greedy algorithms, meta-heuristic algorithms have acceptable efficiency. The ICO algorithm is just recently proposed as an ensign of meta-heuristic algorithms to solve continuous optimization problems in 2022. In this paper, a discrete version of ICO, called DICO, is proposed. The contribution of the proposed method is to present a new intelligent operator based on the nodes' degree and logistic mapping for initialization of solutions. In addition, novel approaches are proposed to reproduce each parent discretely. Another innovation of the proposed method is to present a new local search operator based on the network topology. In this operator, a tabu list is suggested to decrease the selection probability of nodes influenced by those nodes selected previously. The proposed method is evaluated on 6 real- world networks, and it is compared to the state-of-the-art and conventional methods. The evaluation results prove that the proposed method has the higher influence than most of other methods. Also, it has an acceptable performance in terms of computational time making it prominent in comparison with existing methods. [ABSTRACT FROM AUTHOR]