## 1 Introduction
One is introducing new mechanisms, such as opposition-based learning, designing new operators, setting adaptive parameters, etc. [PERSON] [15] introduced a pairwise competition mechanism to update particles in a particle swarm optimization algorithm (PSO) to improve convergence. The positions of particles that lost competitions were updated by learning from winners. [PERSON] [16] designed a virtual crossover operator to improve the search ability of a genetic algorithm (GA). The parent chromosome was divided into several segments and offspring were obtained by exchanging segments based on certain rules. [PERSON] [17] designed an adaptive genetic algorithm to improve searching ability. Probabilities of crossover and mutation operations were dynamically adjusted according to population diversity. Other efforts have been made to design hybrid algorithms by combining strengths of two different algorithms. Algorithms with powerful local searching ability like tabu search (TS) and neighborhood search (NS) are often embedded into other intelligent optimization algorithms to enhance their searching ability [18]. [PERSON] [19] proposed a hybrid algorithm that embedded TS into GA to improve searching ability. The proposed hybrid algorithm combined the excellent global searching ability of GA and powerful local searching ability of TS. [PERSON] [20] proposed an improved hybrid genetic algorithm with variable neighborhood search (VNS) to solve flexible job-shop scheduling problems. A new neighborhood search to identify key processes on the critical path was designed to reduce the number of invalid transformations. [PERSON] [21] proposed a hybrid algorithm combining variable neighborhood search and csc algorithms. VNS was used to improve local search of CS to improve its overall search ability. The research mentioned above improved the searching ability and convergence of algorithms by complicating iterative processes. This means that it is difficult to guarantee the efficiency of algorithms for larger or more complex problems. It is necessary to explore other effective methods to improve algorithms for large-scale inter-cell scheduling problems. Complex network theory is a powerful mathematical tool used to model and analyze complex systems [22; 23]. It reveals properties and functions of various complex systems from interaction relationships and topological structure between elements of complex systems [24]. The theory has been applied to different types of complex systems, such as social networks [25], protein networks [26], public opinion networks [27], and transportation networks [28]. Complex network theory has been applied to the field of manufacturing scheduling in recent years. [PERSON] [29] presented a multiple-attribute decision-makingmethod to identify key machines in production systems. Production systems were modeled as complex networks in which machines were considered as nodes. Complex network features, such as node centrality, were used in the process of identifying key machines. [PERSON] [30] proposed an improved ant colony algorithm based on a complex network model for a flexible job-shop scheduling problem (FJSP). The FJSP was abstracted into a complex network model. The path selection method and pheromone update rule of the ant colony algorithm were tailored to complex network structure and features. [PERSON] [31] established a complex network model in which machines were considered as nodes for an inter-cell scheduling problem. An initial solution generation mechanism based on the modularity of the complex network was proposed to improve the convergence speed and solution quality of a small-world genetic algorithm. The above studies extracted complex network features that could reflect scheduling attributes in corresponding problems. These complex network features reflect features of scheduling problems. Analysis and utilization of these scheduling features has had guiding effects on the optimization of manufacturing scheduling.