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The solutions occurring in natural systems, including the mechanisms that coordinate movement and work of a community, are also applied in many real problems. The test results presented here indicate a potential prospect of application of the swarm-based methods to generate more and more challenging two-dimensional mazes.īased on the observations of the collective behaviour of the swarms of insects or herds of vertebrates, learning from each other and co-operating jointly for a common objective, a new field of computational intelligence was developed: swarm intelligence. Owing to the uniqueness of the problem, consisting in the maze modification, a methodology was developed to make it possible for the individuals belonging to their population to make various types of movements, e.g., approach the best individual, within the range of visibility, or relocate randomly. Secondly, we focus on the well-known concept of the maze complexity given as the total complexity of the path and all branches. Firstly, the complexity of the path was assumed to be a quality criterion, depending on the number of bends and the length of the path between two set points that was subjected to maximisation. When solving such a problem, two complexity measures are used. This paper presents a possibility to apply a swarm-based algorithm, modelled after the behaviour of individuals operating within a group where individuals move around in the manner intended to avoid mutual collisions, to create the most challenging maze developed on a board with determined dimensions. Swarm intelligence draws its inspiration from the collective behaviour of many individual agents interacting with both one another and their environment.