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A performance comparison of PSO and GA in scheduling hybrid flow-shops with multiprocessor tasks

Published: 16 March 2008 Publication History

Abstract

Applications in industry and computing require a proper scheduling of tasks to achieve good performance. The algorithms presented in this paper tackles task scheduling problem in a multi layer multiprocessor environment. Using the scheduling terminology, problem is defined as multiprocessor task scheduling in hybrid flow-shops. This paper presents a particle swarm optimization (PSO) algorithm for the solution. In order to improve the performance of PSO, hybrid techniques were also employed. The performance results, compared with other well known meta-heuristics from the literature, are reported. Results show that PSO and hybrid methods have merits in solving multiprocessor task scheduling in hybrid flow-shop environment.

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Cited By

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  • (2023)A Heuristic-Based Adaptive Iterated Greedy Algorithm for Lot-Streaming Hybrid Flow Shop Scheduling Problem with Consistent and Intermingled Sub-LotsSensors10.3390/s2305280823:5(2808)Online publication date: 3-Mar-2023
  • (2015)An embedded architecture for real-time object detection in digital images based on niching particle swarm optimizationJournal of Real-Time Image Processing10.1007/s11554-012-0256-710:1(75-89)Online publication date: 1-Mar-2015
  • (2011)A hybrid approach for learning concept hierarchy from Malay text using artificial immune networkNatural Computing: an international journal10.1007/s11047-010-9228-710:1(275-304)Online publication date: 1-Mar-2011
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  1. A performance comparison of PSO and GA in scheduling hybrid flow-shops with multiprocessor tasks

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cover image ACM Conferences
SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
March 2008
2586 pages
ISBN:9781595937537
DOI:10.1145/1363686
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: 16 March 2008

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Author Tags

  1. flow shops
  2. hybrid particle swarm optimization
  3. multiprocessor task scheduling

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SAC '08: The 2008 ACM Symposium on Applied Computing
March 16 - 20, 2008
Fortaleza, Ceara, Brazil

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2023)A Heuristic-Based Adaptive Iterated Greedy Algorithm for Lot-Streaming Hybrid Flow Shop Scheduling Problem with Consistent and Intermingled Sub-LotsSensors10.3390/s2305280823:5(2808)Online publication date: 3-Mar-2023
  • (2015)An embedded architecture for real-time object detection in digital images based on niching particle swarm optimizationJournal of Real-Time Image Processing10.1007/s11554-012-0256-710:1(75-89)Online publication date: 1-Mar-2015
  • (2011)A hybrid approach for learning concept hierarchy from Malay text using artificial immune networkNatural Computing: an international journal10.1007/s11047-010-9228-710:1(275-304)Online publication date: 1-Mar-2011
  • (2010)Fuzzy swarm diversity hybrid model for text summarizationInformation Processing and Management: an International Journal10.1016/j.ipm.2010.03.00446:5(571-588)Online publication date: 1-Sep-2010
  • (2009)An optimum design procedure for an aerodynamic radial diffuser with incompressible flow at different Reynolds numbersProceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy10.1243/09576509JPE797224:1(69-84)Online publication date: 8-Sep-2009

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