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Abstract.A strategy for solving an optimisation problem with a large number of objectives by transforming the original objective vector into a two-objective vector during survival selection is presented. The transformed objectives, referred to as preference objectives, consist of a winning score and a vicinity index. The winning score, a maximisation criterion, describes the difference of the number of superior and inferior objectives between two solutions. The minimisation vicinity index describes the level of solution clustering around a search location, particularly the best value of each individual objective, is used to encourage the results to spread throughout the Pareto front. With this strategy, a new multi-objective algorithm, the compressed-objective genetic algorithm (COGA), is introduced. COGA is subsequently benchmarked against a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto genetic algorithm (SPEA-II) in six scalable DTLZ benchmark problems with three to six objectives. The results reveal that the proposed strategy plays a crucial role in the generation of a superior solution set compared to the other two techniques in terms of the solution set coverage and the closeness to the true Pareto front. Furthermore, the spacing of COGA solutions is very similar to that of SPEA-II solutions. Overall, the functionality of the multi-objective evolutionary algorithm (MOEA) with preference objectives is effectively demonstrated.

Arduino kits were used in place of general microcontrollers in a mechatronics course. The hand-on learning environment were mostly self-learned with provided learning kits, tutorials and examples from online resources with lecturers as facilitators. Efficient learning could be achieved with 20-30 students in groups of 4 or 5 against 1 lecturer. The theory was assessed by a written exam from which comparable performances to previous years were observed. The learning kits were also used for the semester project in which students had a limited budget for parts and access to a small-scale manufacturing facility.

This paper presented an initial experience in implanting a full CDIO (Conceive -Design -ImplementOperate) procedure in a mechatronics course. Students learnt and completed product development projects with the university community as users. The course was highly successful with the net promoter score of 81% from students while the lecturers were very pleased. The design thinking modules could be easily added to the beginning and the end of a traditional course with high effectiveness. The key to success was to have an expert in implementing the design thinking to walk the class and lecturers alike through the process.

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