Overview on Binary Optimization Using Swarm-Inspired Algorithms

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents - swarm (e.g. fish, bird, and ant) - directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarm-based techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach - binary decision variables and binary search space - is more efficient for binary optimisation in accuracy and computational cost.

Automated summary

Swarm Intelligence is widely used in optimization problems due to its robustness, scalability, generality, and flexibility, but selecting appropriate swarm-based algorithms for binary optimization remains challenging. Through an analysis of 403 scientific papers, it was found that binary swarm-based algorithms are more efficient in addressing binary optimization problems.