This paper describes a novel method for attacking constrained optimisation problems with evolutionary algorithms, and demonstrates its effectiveness over a range of problems. COMOGA (Constrained Optimisation by MultiObjective Genetic Algorithms) combines two evolutionary techniques for multiobjective optimisation with a simple regulatory mechanism to produce a constrained optimisation method. I...

Despite recent progress in optimisation techniques, finite element stability analysis of realistic three-dimensional (3D) problems is still hampered by the size of the resulting optimisation problem. Current solvers may take a prohibitive computational time, if they give a solution at all. Possible remedies to this are the design of adaptive deremeshing techniques, decomposition of the system o...

This paper proposes a simple and interesting algorithm dealing with structural shape, topology and thickness optimisation simultaneously. The proposed approach is based on an interesting idea of migrating boundary nodes in an iterative manner. An intuitive nodal-based evolutionary structural algorithm drives the optimisation process. Finite element analysis is required at each stage to reveal t...

The overall complexity of optimisation problems in ship design results in high computation time requirements. The high computation time requirements for objective function evaluations in a complex design optimisation problem can practically be reduced by either using parallel execution of the objective functions in state-of-the-art super computers or concurrent execution of the objective functi...

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at t...

Particle swarm optimisation (PSO) is a biologically-inspired, population-based optimisation technique that has been successfully applied to various problems in science and engineering. In the context of semantic technologies, optimisation problems also occur but have rarely been considered as such. This work addresses the problem of ontology alignment, which is the identification of overlaps in...

The CFD method is used to predict the flow and the compressor map. For the optimisation CAE-based parametric optimisation with respect to 15 geometry parameters, based on the primary design is used. The optimisation procedure is divided in two steps: The first one is the sensitivity study combined with the generation of the Metamodel of optimal Prognosis (MoP), where the most relevant input par...

In this thesis, we devise a new stochastic optimlsation method (cascade optimisation algorithm) by incorporating the concepts from Markov process whilst eliminating the inherent sequential nature that is the major deficit preventing the exploitation of advances in distributed computing infrastructures. This method introduces partitions and pools to store intermediate solution and corresponding ...

A software platform for global optimisation, called PaGMO, has been developed within the Advanced Concepts Team (ACT) at the European Space Agency, and was recently released as an open-source project. PaGMO is built to tackle high-dimensional global optimisation problems, and it has been successfully used to find solutions to real-life engineering problems among which the preliminary design of ...

T competitiveness and dynamic nature of today’s marketplace is due to rapid advances in information technology, short product life cycles and the continuing trend in global outsourcing. Managing the resulting supply chain networks effectively is challenged by high levels of uncertainty in supply and demand, confl ict objectives, vagueness of information, numerous decision variables and constrai...