Asset managers are faced with the necessity to reduce total life-cycle costs of their facilities as well as to improve their physical condition and long-term performance while considering the relative consequences of their failure. This paper presents a systematic decision-making approach for asset maintenance management that combines a stochastic Markovian performance prediction model with a m...

Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of fronta...

In this paper we explore established methods for optimising multi-objective functions whilst addressing the problem of preliminary design. Methods from the literature are investigated and new ones introduced. All methods are evaluated within a collaborative project for whole system airframe design and the basic problems and difficulties of preliminary design methodology are discussed (Cvetković...

This paper describes the first application of the Bees Algorithm to multi-objective optimisation problems. The Bees Algorithm is a search procedure inspired by the way honey bees forage for food. A standard mechanical design problem, the design of a welded beam structure, was used to benchmark the Bees Algorithm. The results obtained show the robust performance of the Bees Algorithm.

Interaction among decision variables is inherent to a number of real-life engineering design optimisation problems. There are two types of interaction that can exist among decision variables: inseparable function interaction and variable dependence. The aim of this paper is to propose an Evolutionary Computing (EC) technique for handling variable dependence in multi-objective optimisation probl...