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ICSPEA: evolutionary five-axis milling path optimisation

Published: 07 July 2007 Publication History

Abstract

ICSPEA is a novel multi-objective evolutionary algorithm which integrates aspects from the powerful variation operators of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the well proven multi-objective Strength Pareto Evaluation Scheme of the SPEA 2. The CMA-ES has already shown excellent performance on various kinds of complex single-objective problems. The evaluation scheme of the SPEA 2 selects individuals with respect to their current position in the objective space using a scalar index in order to form proper Pareto front approximations. These indices can be used by the CMA-part of ICSPEA for learning and guiding the search towards better Pareto front approximations. The ICSPEA is applied to complex benchmark functions such as an extended n-dimensional Schaffer's function or Quagliarella's problem. The results show that the CMA operator allows ICSPEA to find the Pareto set of the generalised Schaffer's function faster than SPEA 2. Furthermore, this concept is tested on the complex real-world application of the multi-objective optimization of five-axis milling NC-paths. An application of ICSPEA to the milling-path optimisation problem yielded efficient sets of five-axis NC-paths.

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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Author Tags

  1. CMA-ES
  2. SPEA 2
  3. evolutionary computing
  4. mechanical engineering
  5. multi-objective optimisation

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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