Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms

Product modularity : a multi-objective configuration approach

Product modularity is often seen as a means by which a product system can be decomposed into smaller, more manageable chunks in order to better manage design, manufacturing and after-sales complexity. The most common approach is to decompose the product down to component level and then group the components to form modules. The rationale for module grouping can vary, from the more technical physical and functional component interactions, to any number of strategic objectives such as variety, maintenance and recycling. The problem lies with the complexity of product modularity under these multiple (often conflicting) objectives. The research in this thesis presents a holistic multi-objective computer aided modularity optimisation (CAMO) framework. The framework consists of four main steps: 1) product decomposition; 2) interaction analysis; 3) formation of modular architectures and; 4) scenario analysis. In summary of these steps: the product is first decomposed into a number a basic components by analysis of both the physical and functional product domains. The various dependencies and strategic similarities that occur between the product s components are then analysed and entered into a number of interaction matrixes. A specially developed multi-objective grouping genetic algorithm (MOGGA) then searches the matrices and provides a whole set of alternative (yet optimal) modular product configurations. The solution set is then evaluated and explored (scenario analysis) using the principles of Analytic Hierarchy Process. A software prototype has been created for the CAMO framework using Visual Basic to create a multi-objective genetic algorithm (GA) based optimiser within an excel environment. A case study has been followed to demonstrate the various steps of the framework and make comparisons with previous works. Unlike previous works, that have used simplistic optimisation algorithms and have in general only considered a limited number of modularisation objectives, the developed framework provides a true multi-objective approach to the product modularisation problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo