Abstract
Tool selection for roughing components is a complex problem. Attempts to automate the process are further complicated by computationally expensive evaluations. In previous work we assessed the performance of several single-objective metaheuristic algorithms on the tool selection problem in rough machining and found them to successfully return optimal solutions using a low number of evaluations, on simple components. However, experimenting on a more complex component proved less effective. Here we show how search success can be improved by multi-objectivizing the problem through constraint relaxation. Operating under strict evaluation budgets, a multiobjective algorithm (NSGA-II) is shown to perform better than single-objective techniques. Further improvements are gained by the use of guided search. A novel method for guidance, “Guided Elitism”, is introduced and compared to the Reference Point method. In addition, we also present a modified version of NSGA-II that promotes more diversity and better performance with small population sizes.
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Churchill, A.W., Husbands, P., Philippides, A. (2013). Multi-objectivization of the Tool Selection Problem on a Budget of Evaluations. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_45
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DOI: https://doi.org/10.1007/978-3-642-37140-0_45
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