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Constrained multi-objective aerodynamic shape optimization via swarm intelligence

Published: 12 July 2014 Publication History

Abstract

In this paper, we present a Multi-objective Particle Swarm Optimizer (MOPSO) based on a decomposition approach, which is proposed to solve Constrained Multi-Objective Aerodynamic Shape Optimization Problems (CMO-ASOPs). The constraint-handling technique adopted in this approach is based on the well-known epsilon-constraint method. Since the ε-constraint method was initially proposed to deal with constrained single-objective optimization Problems, we adapted it so that it could be incorporated into a MOPSO. Our main focus is to solve CMO-ASOPs in an efficient and effective manner. The proposed constrained MOPSO guides the search by updating the position of each particle using a set of solutions considered as the global best according to both the decomposition approach and the epsilon-constraint method. Our preliminary results indicate that our proposed approach is able to outperform a state-of-the-art MOEA in several CMO-ASOPs.

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  • (2024)Dynamic Constrained Multiobjective Algorithm Based on Feasible Region PredictionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664110(1536-1543)Online publication date: 14-Jul-2024
  • (2024)Multipopulation Evolution-Based Dynamic Constrained Multiobjective Optimization Under Diverse Changing EnvironmentsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.324176228:3(763-777)Online publication date: Jun-2024
  • (2024)Evolutionary constrained multi-objective optimization: a reviewVicinagearth10.1007/s44336-024-00006-51:1Online publication date: 4-Jul-2024
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Published In

cover image ACM Conferences
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
July 2014
1478 pages
ISBN:9781450326629
DOI:10.1145/2576768
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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Publication History

Published: 12 July 2014

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Author Tags

  1. aerodynamic shape optimization
  2. constrained multi-objective optimization
  3. multi-objective swarm optimization

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  • Research-article

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

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  • (2024)Dynamic Constrained Multiobjective Algorithm Based on Feasible Region PredictionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664110(1536-1543)Online publication date: 14-Jul-2024
  • (2024)Multipopulation Evolution-Based Dynamic Constrained Multiobjective Optimization Under Diverse Changing EnvironmentsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.324176228:3(763-777)Online publication date: Jun-2024
  • (2024)Evolutionary constrained multi-objective optimization: a reviewVicinagearth10.1007/s44336-024-00006-51:1Online publication date: 4-Jul-2024
  • (2023)Multiobjective Differential Evolution With Speciation for Constrained Multimodal Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319425327:4(1115-1129)Online publication date: Aug-2023
  • (2023)A Survey on Evolutionary Constrained Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.315553327:2(201-221)Online publication date: Apr-2023
  • (2023)A dynamic dual-population co-evolution multi-objective evolutionary algorithm for constrained multi-objective optimization problemsApplied Soft Computing10.1016/j.asoc.2023.110311141(110311)Online publication date: Jul-2023
  • (2022)A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimizationArchives of Computational Methods in Engineering10.1007/s11831-022-09859-930:3(2181-2209)Online publication date: 9-Dec-2022
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  • (2019)The Interactive Design Approach for Aerodynamic Shape Design Optimisation of the Aegis UAVAerospace10.3390/aerospace60400426:4(42)Online publication date: 8-Apr-2019

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