Investigating Knowledge-Based Exploration-Exploitation Balance in a Minimalist Swarm Optimiser

One of the key challenges in evolutionary, swarm and population-based optimisers is the balance between exploration and exploitation. The reliance on both guided and stochastic search in these algorithms allows researchers to take different approaches to the topic. This work uses a minimalist, vector-stripped swarm optimiser to present a theoretical analysis on the behaviour of the particles. Being a population-based continuous optimiser, dispersive flies optimisation or DEO, bears several similarities with the well-known particle swarm optimisers, differential evolution algorithms and their bare-bones variants. The distinctive feature of this algorithm is its sheer reliance on particles positions to update the population. The minimalist nature of the algorithm reduces the challenges of understanding particles oscillation around constantly changing centres, particles' influence on one another, and their trajectories. This work presents a unified exploration-exploitation probability study which is derived from six scenarios in order to examine the population's dimensional behaviour in each iteration. This paves the way to propose and investigate adaptable, diversity promoting mechanisms. The proposed methods, which may be extendable to other optimisers, are then examined on a comprehensive set of benchmarks, and finally applied to high-dimensional tomographic reconstruction which is an important inverse problem in medical and industrial imaging.