The aim of influence maximization problem is to mine a small set of influential individuals in a complex network which could reach the maximum influence spread. In this paper, an efficient fitness function based on local influence is designed to estimate the influence spread. Then, we propose a discrete particle swarm optimization based algorithm to find the final set with the maximum value of the fitness function. In the proposed algorithm, discrete position and velocity are redefined and problem-specific update rules are designed. In order to accelerate the convergence, we introduce a degree-based population initialization method and a mutation learning based local search strategy. Experimental results compared with four comparison algorithms show that our proposed algorithm is able to efficiently find good-quality solutions.
