Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. In SI, an individual has a simple structure and its function is single. However, such systems composed by many individuals show the phenomenon of emergence and can address several difficult real-world problems that are impossible to be solved by only an individual. During recent decades, SI methods have been successfully applied to cope with complex and time-consuming problems that are hard to be solved by traditional mathematical methods. Therefore, SI is indeed a topic of interest amongst researchers in various fields of science and engineering. Some popular SI paradigms, including ant colony optimization, and particle swarm optimization, have been successfully applied to handle various practical engineering problems.

Combinatorial optimization is a subset of mathematical optimization related to operational research, algorithm theory, and computational complexity theory. It has important applications in several fields, including artificial intelligence, machine learning, mathematics, auction theory, and software engineering. Many real-world problems can be modeled and solved as combinatorial optimization problems. This is an active research area, where new formulations, algorithms, practical applications, and theoretical results are often proposed and published. Current challenges in the field involve modeling of hard problems, development of exact methods, design and experimental evaluation of approximate and hybrid methods, among others.

The overall aim of this special issue is to compile the latest research and development, up-to-date issues, and challenges in the field of SI and its applications in combinatorial optimization. Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view. Potential topics include, but are not only limited to:  

Swarm Intelligence Algorithms

• Improvements of traditional SI methods (e.g., ant colony optimization and particle swarm optimization)

• Recent development of SI methods (e.g., monarch butterfly optimization, earthworm optimization algorithm, elephant herding optimization, moth search algorithm, bird swarm algorithm, chicken swarm optimization, fireworks algorithm, and brain storm optimization)

• Theoretical study on SI algorithms using various techniques (e.g., Markov chain, dynamic system, complex system/networks, and Martingale)

Applications in Combinatorial Optimization

• Scheduling (e.g., vehicle rescheduling, nurse scheduling problem, flow shop scheduling, and fuzzy scheduling)

• Traveling salesman problem (e.g., symmetric traveling salesman problem, asymmetric traveling salesman problem, fuzzy traveling salesman problem, and other real-world problems that can be converted to traveling salesman problem)

• Knapsack problem (e.g., 0/1 knapsack problem, multi-objective knapsack problem, multi-dimensional knapsack problem, multiple knapsack problem, and quadratic knapsack problem)

• Others (e.g., constraint satisfaction problem, set cover problem, task assignment problem, and portfolio optimization)