Genetic Algorithm with Dynamic Fitness Sharing Niching Method for Multimodal Opinion Maximization Problem

Social networks built on real or online provide platforms for people to share and update their opinions. Based on the influence maximization problem, the opinion maximization (OM) problem aims to locate a set of initial nodes to achieve the maximum of total positive opinion dissemination in the social network. However, decision-makers prefer to have multiple optimal or near-optimal solutions at hand, which brings up the multimodal OM problem. In this paper, we firstly define the multimodal OM problem, which aims at providing several promising sets of initial nodes at one time. To solve this problem, we propose a genetic algorithm with a dynamic fitness sharing niching method (GADN). In GADN, we take the dynamic fitness sharing method to divide the population dynamically, design a repair strategy to fill the possible gap in sets after crossover, and take a scalable reproduction to assign reproductive opportunities. Finally, a series of experiments are conducted on multiple social networks. The results show that the proposed GADN outperforms other methods on both opinion and active ratio in most cases.