Cooperation of Nature and Physiologically Inspired Mechanisms in Visualisation

Abstract

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants -Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect . We also discuss whether or not the 'art works' generated by nature and biologically inspired algorithms can possibly be considered as 'computationally creative'.

Swarmic Sketches and Attention Mechanism

“…The general behaviour of the swarms in the context of computational creativity has been extensively discussed in previous works ( [1,2,5]), touching upon the concepts of freedom and constraint and their impact on mapping these two prerequisites of creativity onto the two well-known phases of exploration and exploitation in swarm intelligence algorithms. Although most swarm intelligence algorithm have their internal exploration and exploitation phases, in this work, …”

“…trajectory of the particles moving from position (x 0 , y 0 ) to (x 1 , y 1 ) and so forth). The adopted PSO algorithm is briefly presented in Section 2.2 (more technical details on the behaviour of particles are reported in a previous publication [1]). …”

“…where w is the inertia weight whose optimal value is problem dependent [17]; v t−1 id is the velocity component of particle i in dimension d at time step t− 1; c 1,2 are the learning factors (also referred to as acceleration constants) for personal best and neighbourhood best respectively (they are constant); r 1,2 are random numbers adding stochasticity to the algorithm and they are drawn from a uniform distribution on the unit interval U (0, 1); p id is the personal best position of particle x i in dimension d; and g id is the neighbourhood best. Therefore, PSO optimisation is based on particles' individual experience and their social interaction with other particles.…”

“…[4]), their artistic capabilities have been detailed in several publications along with some philosophical arguments on computational creativity (e.g. [1,5,2]). …”

Identifying metastasis in bone scans with Stochastic Diffusion Search

“…This algorithm has been used alongside other swarm intelligence algorithms in several fields (e.g. [3], [4], [5], [6]). …”