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Design optimization of MEMS using constrained multi-objective evolutionary algorithm

Published: 12 July 2014 Publication History

Abstract

MEMS layout optimization is a typical multi-objective constrained optimization problem. This paper proposes an improved MOEA called cMOEA/D to solve this problem. The cMOEA/D is based on MOEA/D but also uses the frequency of individual update of sub-problems to locate the promising sub-problems. By dynamically allocating computing resources to more promising sub-problems, we can effectively improve the performance of the algorithm to find more non-dominated solutions in MEMS layout optimization. In addition, we compared two mechanisms of constraint handling, Stochastic Ranking (SR) and Constraint-domination principle (CDP). The experimental results show that CDP works better than SR and the proposed algorithm outperforms the state-of-art algorithms such as NSGA-II and MOEA/D, in terms of convergence and diversity.

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Cited By

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  • (2022)Handling Constrained Multiobjective Optimization Problems via Bidirectional CoevolutionIEEE Transactions on Cybernetics10.1109/TCYB.2021.305617652:10(10163-10176)Online publication date: Oct-2022

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cover image ACM Conferences
GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
July 2014
1524 pages
ISBN:9781450328814
DOI:10.1145/2598394
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: 12 July 2014

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Author Tags

  1. mems layout design
  2. multi-objective constrained optimization

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GECCO '14
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GECCO '14: Genetic and Evolutionary Computation Conference
July 12 - 16, 2014
BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2022)Handling Constrained Multiobjective Optimization Problems via Bidirectional CoevolutionIEEE Transactions on Cybernetics10.1109/TCYB.2021.305617652:10(10163-10176)Online publication date: Oct-2022

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