Ensuring manufacturing reliability is key to satisfying product orders when production plants are subject to disruptions. Reliability of a supply network is closely related to the redundancy of products as production in disrupted plants can be replaced by alternative plants. However the benefits of incorporating redundancy must be balanced against the costs of doing so. Models in literature are...

A multi-objective genetic algorithm is developed for the purpose of optimizing the rule-base of a Self-Organising Fuzzy Logic Control algorithm (SOFLC). The tuning of the SOFLC optimization is based on selection of the best shaped performance index for modifying the rulebase on-line. A comparative study is conducted between various methods of multi-objective genetic optimisation using the SOFLC...

In this work, we propose a computational framework to design in silico robust bacteria able to overproduce multiple metabolites. To this end, we search the optimal genetic manipulations, in terms of knockout, which also guarantee the growth of the organism. We introduce a multi-objective optimisation algorithm, called Genetic Design through Multi-Objective (GDMO), and test it in several organis...

Clustering-based Leaders’ Selection (CLS) is a novel approach for leaders selection in multi-objective particle swarm optimisation. Both objective and solution spaces are clustered. An indirect mapping between clusters in both spaces is defined to recognize regions with potentially better solutions. A leaders archive is built which contains representative particles of selected clusters in the o...

This paper proposes an improved version of volume dominance to assign fitness to solutions in Pareto-based multi-objective optimisation. The impact of this revised volume dominance on the performance of multi-objective evolutionary algorithms is investigated by incorporating it into three approaches, namely SEAMO2, SPEA2 and NSGA2 to solve instances of the 2-, 3and 4objective knapsack problem. ...

In this paper we present a genetic algorithm based system for conceptual engineering design. First, we present a method based on preference relations for transforming non–crisp (qualitative) relationships between objectives in multi–objective optimisation into quantitative attributes (numbers). This is integrated with two multi– objective Genetic Algorithms: weighted sums GA and a method for co...

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The design of rolling bearings has to satisfy various constraints, e.g. the geometrical, kinematics and the strength, while delivering excellent performance, long life and high reliability. This invokes the need of an optimal design methodology to achieve these objectives collectively, i.e. the multi-objective optimisation. In this paper, three primary objectives for a rolling bearing, namely, ...

This chapter examines the performance characteristics of both asynchronous and synchronous parallel particle swarm optimisation algorithms in heterogeneous, fault-prone environments. The chapter starts with a simple parallelisation paradigm, the Master-Slave model using Multi-Objective Particle Swarm Optimisation (MOPSO) in a heterogeneous environment. Extending the investigation to general, di...