Improving problem definition through interactive evolutionary computation
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Finite Elements in Analysis and Design
Efficient aerodynamic shape optimization in MDO context
Journal of Computational and Applied Mathematics
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A method for group decision making with multi-granularity linguistic assessment information
Information Sciences: an International Journal
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Information Sciences: an International Journal
Multidisciplinary design optimization of an automotive magnetorheological brake design
Computers and Structures
Component-oriented decomposition for multidisciplinary design optimization in building design
Advanced Engineering Informatics
Mathematics and Computers in Simulation
Expert Systems with Applications: An International Journal
Advances in Engineering Software
A new integrated design concept evaluation approach based on vague sets
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
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This paper presents a goal programming algorithm utilising a weight-free aggregate function for producing enhanced design alternatives and a knowledge-based procedure for the selection of the final solution from a pool of enhanced alternatives. Normally, in a multi-disciplinary design problem several teams of designers with different preference and background knowledge are involved in the decision making processes, such as constructing aggregate functions for multi-objective optimisation and trade-off study towards selecting the final solution. In constructing an aggregate function, designers need to identify how important each objective is with respect to the other objectives. However, in the absence of a final decision maker with expertise in all disciplines, the predicates such as ''as important as'' or ''more important than'' cannot be used to compare objectives from different disciplines, and therefore the establishment of a weighted aggregate function is not viable. Introducing the concepts of unsatisfactoriness and tolerated margin, ''how important is a design quality with respect to other design qualities'' is replaced with ''to what extent can the unsatisfactoriness of a design quality be tolerated''. This removes the predicament arising from the usual subjective decision making when forming an aggregate function and also transforms the final solution selection from a negotiation process to a straightforward and knowledge based procedure. A software tool comprising of two modules, a multi-deme genetic algorithm, for producing enhanced alternatives, and an assessment module, which includes visualisation, ranking and filtering facilities, is developed and its performance is shown using an illustrative multi-disciplinary design space.