Efficient optimisation of structures using tabu search

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 example a truss is optimised to minimise mass and the results compared to results obtained using an alternative TS implementation. In the second example, the problem has multiple objectives that are compounded into a single objective function value using game theory. In general the results demonstrate that the TS method is capable of solving structural optimisation problems at least as efficiently as other numerical optimisation approaches

Individual and global adaptation in networks

The structure of complex biological and socio-economic networks affects the selective pressures or behavioural incentives of components in that network, and reflexively, the evolution/behaviour of individuals in those networks changes the structure of such networks over time. Such ‘adaptive networks’ underlie how gene-regulation networks evolve, how ecological networks self-organise, and how networks of strategic agents co-create social organisations. Although such domains are different in the details, they can each be characterised as networks of self-interested agents where agents alter network connections in the direction that increases their individual utility. Recent work shows that such dynamics are equivalent to associative learning, well-understood in the context of neural networks. Associative learning in neural substrates is the result of mandated learning rules (e.g. Hebbian learning), but in networks of autonomous agents ‘associative induction’ occurs as a result of local individual incentives to alter connections. Using results from a number of recent studies, here we review the theoretical principles that can be transferred between disciplines as a result of this isomorphism, and the implications for the organisation of genetic, social and ecological networks

Max-Weight Revisited: Sequences of Non-Convex Optimisations Solving Convex Optimisations

We investigate the connections between max-weight approaches and dual subgradient methods for convex optimisation. We find that strong connections exist and we establish a clean, unifying theoretical framework that includes both max-weight and dual subgradient approaches as special cases. Our analysis uses only elementary methods, and is not asymptotic in nature. It also allows us to establish an explicit and direct connection between discrete queue occupancies and Lagrange multipliers.Comment: convex optimisation, max-weight scheduling, backpressure, subgradient method

Endogenous population subgroups: the best population partition and optimal number of groups

The aim of this paper is to suggest a method to find endogenously the points that group the individuals of a given distribution in k clusters, where k is endogenously determined. These points are the cut-points. Thus, we need to determine a partition of the N individuals into a number k of groups, in such way that individuals in the same group are as alike as possible, but as distinct as possible from individuals in other groups. This method can be applied to endogenously identify k groups in income distributions: possible applications can be povertystopping rule, optimal grouping, Gini index

On Validating Closed-Loop Behaviour from Noisy Frequency-Response Measurements

It is shown how noisy closed-loop frequency-response measurements can be used to obtain pointwise in frequency bounds on the possible difference between the actual closed-loop system and the closed-loop comprising a nominal model of the plant and the stabilising controller. To this end, Vinnicombe's gap metric framework for robustness analysis plays a central role. Indeed, an optimisation problem and corresponding algorithm are proposed for estimating the chordal distance between the frequency responses of the nominal plant model and a plant that is consistent with the closed-loop data and a priori information, when projected onto the Riemann sphere