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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- Multi-objectivization of the Tool Selection Problem on a Budget of Evaluations
Alexander W. Churchill
- Springer Berlin Heidelberg
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