Recent advances in computer architecture has started to in uence the eld of numerical optimisation. Several methods to solve large mathematical programming problems which were tried and given up in the last three decades has gained renewed interest. stochastic optimisation is such area which has a huge potential of providing successful results due to its high parallel content. In most practical...

In spite of many applications of evolutionary algorithms in optimisation, theoretical results on the computation time and time complexity of evolutionary algorithms on different optimisation problems are relatively few. It is still unclear when an evolutionary algorithm is expected to solve an optimisation problem efficiently or otherwise. This paper gives a general analytic framework for analy...

The introduction of energy efficiency as a new goal into already complex production plans is a difficult challenge. Decision support systems can help with this problem but these systems are often resisted by end users who ultimately bear the responsibility for production outputs. This paper describes the design of a decision support tool that aims to increase the interpretability of decision su...

We outline a method for distributed Monte Carlo optimisation of computational problems in networks of agents, such as peer-to-peer networks of computers. The optimisation and messaging procedures are inspired by gossip protocols and epidemic data dissemination, and are decentralised, i.e. no central overseer is required. In the outlined method, each agent follows simple local rules and seeks fo...

Discrete optimisation problems arise in many different areas and are studied under many different names. In many such problems the quantity to be optimised can be expressed as a sum of functions of a restricted form. Here we present a unifying theory of complexity for problems of this kind. We show that the complexity of a finite-domain discrete optimisation problem is determined by certain alg...

This paper presents a new algorithm for enhancing the efficiency of simulation-based optimisation using local search and neural network metamodels. The local search strategy is based on steepest ascent Hill Climbing. In contrast to many other approaches that use a metamodel for simulation optimisation, this algorithm alternates between the metamodel and its underlying simulation model, rather t...

This paper presents a novel approach to the optimisation of structures using a Tabu search (TS) method. TS is a metaheuristic which is used to guide local search methods towards a globally optimal solution by using flexible memory cycles of differing time spans. Results are presented for the well established ten bar truss problem and compared to results published in the literature. In the first...

The execution process of a evolutionary algorithm typically involves some trial-and-error. This is due to the difficulty in setting the initial parameters of the algorithm especially when little is known about the problem domain. This problem is magnified when applied to many-objective optimisation, as care is needed to ensure that the final population of candidate solutions is representative o...

Splines with free knots have been extensively studied in regard to calculating the optimal knot positions. The dependence of the accuracy of approximation on the knot distribution is highly nonlinear, and optimisation techniques face a difficult problem of multiple local minima. The domain of the problem is a simplex, which adds to the complexity. We have applied a recently developed cutting an...

The application of two evolutionary optimisation schemes to the problem of flight clearance of nonlinear control laws for highly augmented aircraft is described. Hybrid versions of both schemes incorporating local gradient-based optimisation are also developed and evaluated. Comparisons of computational complexity and global convergence are conducted by checking for violations of a nonlinear cl...