Swarm intelligence emerges from the collective intelligent efforts of massive numbers of autonomous individuals, which are motivated to carry out challenging computational tasks under a certain network-based organizational structure. The main principle of swarm intelligence derives from the simulation of the intelligent behavior of social biological swarms in nature. Based on different natural collaborative behaviors such as labor division, adaptive foraging, and coevolution, swarms have promising capabilities of self-organization, self-adaptation, and self-learning, and can create powerful intelligent behavior to complete complex tasks, beyond the limits of individual intelligence. Therefore, exploring the evolution mechanisms of complex swarm intelligence systems is important for the application of the emerged swarm intelligence.

Recent attention has focused more on the theoretical models, methods, and applications of complex swarm intelligence systems, such as evolutionary computation systems, multi-agent systems, and social computation systems. A series of model mechanisms have been developed for the emergence and evolution of swarm intelligence from different perspectives. Recently, with the development of the Internet, the collaborative behaviors of human society have further broadened the scope of swarm intelligence, and also raise new challenges to data analysis, modeling, simulation, decision-making, and applications (e.g., big data) of swarm intelligence and evolutionary computation. In particular, the analytics, modeling, and simulation mechanisms aim to reveal how the swarms create intelligent behavior, which is often evolving with the change of the environment, and the cooperative decision-making targets to investigate how to coordinate the organizational structure for improving the emergence and evolution of intelligence.

This Special Issue will focus on bringing both experts and newcomers from either academia or industry together to discuss new and existing issues concerning the data analysis, modeling, simulation, decision-making, and applications of swarm intelligence and evolutionary computation. This Issue aims to encourage the integration between academic research and industry applications, and to stimulate further engagement with the user community. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Modeling of swarm intelligence systems
• Simulation of emerged swarm intelligence
• Decision-making of swarm intelligence systems
• Multi-agent systems
• Social computation
• Swarm optimization methods
• Bio-inspired computation algorithms
• Data-driven multi-objective evolutionary computation
• High-dimensional and many-objective evolutionary algorithms
• Data analytics for large-scale complex networks
• Swarm intelligence techniques for business intelligence, finance, healthcare, bioinformatics, intelligent transportation, smart city, smart sensor networks, cybersecurity, and other critical application areas