The Quantum Approximate Optimisation Algorithm is a $p$ layer, time-variable split operator method executed on quantum processor and driven to convergence by classical outer loop optimisation. co-processor varies individual application times of problem/driver propagator sequence prepare state which approximately minimizes the problem's generator. Analytical solutions choose optimal (called angl...

An alternative approach, between much others, for mathematical representation of dynamics systems with complex or chaotic behaviour, is a radial basis function neural network using k-means for clustering and optimized by pseudo-inverse and particle swarm optimisation. This paper presents the implementation and study to identify a dynamic system, with nonlinear and chaotic behaviour, called Röss...

Emergency departments (EDs) have been becoming increasingly congested due to the combined impacts of growing demand, access block and increased clinical capability of the EDs. This congestion has known to have adverse impacts on the performance of the healthcare services. Attempts to overcome with this challenge have focussed largely on the demand management and the application of system wide p...

The ideas proposed in this work are aimed to describe a novel approach based on artificial life (alife) environments for on-line adaptive optimisation of dynamical systems. The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system evolution (adaptation to environmental changes). The essence could ...

The ideas proposed in this work are aimed to develop a novel approach based on artificial life (alife) environments for on-line adaptive optimisation of dynamical systems. The basic features of the proposed approach are: no intensive modelling (continuos learning directly from measurements) and capability to follow the system evolution (adaptation to environmental changes). The essence could be...

If an optimal solution is very sensitive to perturbations, then it may not be the most appropriate solution to use. In situations where natural variation is inevitable, it is therefore necessary to find a robust optimal solution. This paper examines emerging techniques for finding robust solutions to multiobjective optimisation problems. One of the major concerns in robust optimisation is that ...

Particle swarm optimisation (PSO) algorithm is known for its easy implementation and has been empirically shown to perform well in many optimisation problems. Thus, it is expected to mitigate the computational burden associated with the solutions of non-linear transcendental equations relevant to problems related to power converter systems. This paper starts with an overview of the general conc...