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A domain-specific crossover and a helper objective for generating minimum weight compliant mechanisms

Authors:

Deepak Sharma,

Kalyanmoy Deb,

N. N. KishoreAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1723 - 1724

https://doi.org/10.1145/1389095.1389428

Published: 12 July 2008 Publication History

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

A domain-specific crossover and a helper objective for generating minimum weight compliant mechanisms

Pages 1723 - 1724

Abstract
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Abstract

While designing the Compliant Mechanisms (CM), an equal attention is required on both the problem formulation and the optimization algorithm used. Authors of this paper have successfully proposed the formulation of CM tracing user-defined paths based on the precision points. In this paper, authors modify the NSGA-II algorithm by incorporating (i) a helper objective and (ii) a domain specific crossover which assist in generating a diverse set of non-dominated solutions. First, the single-objective optimization problem of minimizing the weight of structure is solved and named the topology as a reference design. Thereafter, a bi-objective optimization problem is dealt to evolve 'trade-off' solutions for a primary objective of minimizing the weight and a secondary objective of maximizing the diversity with respect to the reference design. Both the optimization problems are solved using a local search based NSGA-II procedure. This study has further compared its results with another GA implementation having a different crossover operator.

References

[1]

K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002.

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[2]

K. Deb and S. Chaudhuri. Automated discovery of innovative designs of mechanical components using evolutionary multiobjective algorithms. In N. Nedjah and L. de Macedo M, editors, Evolutionary Machine Design: Methodology and Applications, chapter 6, pages 143--168. Nova Science Publishers, Inc, New York, 2005.

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[3]

D. Sharma, K. Deb, and N. N. Kishore. Evolving path generation compliant mechanisms (PGCM) using local-search based multi-objective genetic algorithm. In International Conference on Trends in Product Life Cycle, Modeling, Simulation and Synthesis (PLMSS), pages 227--238, December 2006.

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[4]

D. Sharma, K. Deb, and N. N. Kishore. A domain-specific crossover and a helper objective for generating minimum weight compliant mechanisms. Technical Report KanGAL Report No.2008001., Indian Institute of Technology Kanpur, India, 2008.

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[5]

D. Sharma, K. Deb, and N. N. Kishore. Towards generating diverse topologies of path tracing compliant mechanisms using a local search based multi-objective genetic algorithm procedure. Accepted in 2008 IEEE World Congress on Computational Intelligence (WCCI) Conference, 2008.

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Cited By

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Guirguis DAulig NPicelli RZhu BZhou YVicente WIorio FOlhofer MMatusik WCoello Coello CSaitou K(2020)Evolutionary Black-Box Topology Optimization: Challenges and PromisesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.295441124:4(613-633)Online publication date: Aug-2020
https://doi.org/10.1109/TEVC.2019.2954411
Ram LSharma D(2017)Evolutionary and GPU computing for topology optimization of structuresSwarm and Evolutionary Computation10.1016/j.swevo.2016.08.00435(1-13)Online publication date: Aug-2017
https://doi.org/10.1016/j.swevo.2016.08.004
Sharma DDeb K(2014)Generation of Compliant Mechanisms using Hybrid Genetic AlgorithmJournal of The Institution of Engineers (India): Series C10.1007/s40032-014-0127-z95:4(295-307)Online publication date: 26-Jul-2014
https://doi.org/10.1007/s40032-014-0127-z
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A domain-specific crossover and a helper objective for generating minimum weight compliant mechanisms
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
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Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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 2008

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July 12 - 16, 2008

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Guirguis DAulig NPicelli RZhu BZhou YVicente WIorio FOlhofer MMatusik WCoello Coello CSaitou K(2020)Evolutionary Black-Box Topology Optimization: Challenges and PromisesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.295441124:4(613-633)Online publication date: Aug-2020
https://doi.org/10.1109/TEVC.2019.2954411
Ram LSharma D(2017)Evolutionary and GPU computing for topology optimization of structuresSwarm and Evolutionary Computation10.1016/j.swevo.2016.08.00435(1-13)Online publication date: Aug-2017
https://doi.org/10.1016/j.swevo.2016.08.004
Sharma DDeb K(2014)Generation of Compliant Mechanisms using Hybrid Genetic AlgorithmJournal of The Institution of Engineers (India): Series C10.1007/s40032-014-0127-z95:4(295-307)Online publication date: 26-Jul-2014
https://doi.org/10.1007/s40032-014-0127-z
Sharma DDeb KKishore N(2011)Domain-specific initial population strategy for compliant mechanisms using customized genetic algorithmStructural and Multidisciplinary Optimization10.1007/s00158-010-0575-x43:4(541-554)Online publication date: 1-Apr-2011
https://dl.acm.org/doi/10.1007/s00158-010-0575-x
Jingjun Zhang Yanminshang Ruizhen Gao Yuzhen Dong (2010)An improved genetic algorithm based on subdivision theory and fixed point algorithms2010 5th IEEE Conference on Industrial Electronics and Applications10.1109/ICIEA.2010.5514804(1484-1489)Online publication date: Jun-2010
https://doi.org/10.1109/ICIEA.2010.5514804

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Domain-specific initial population strategy for compliant mechanisms using customized genetic algorithm

Helper-objective optimization strategies for the Job-Shop Scheduling Problem

Helper and equivalent objective different evolution for constrained optimisation

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