|1/2019 - 3|
View TOC | « Previous Article | Next Article »
Combinatorial versus Priority Based Optimization in Resource Constrained Project Scheduling Problems by Nature Inspired MetaheuristicsBEJINARIU, S.-I. , COSTIN, H. , COSTIN, D.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,467 KB) | Citation | Downloads: 842 | Views: 1,842|
biological information theory, evolutionary computation, optimization, particle swarm optimization, scheduling algorithms
optimization(18), algorithm(11), swarm(8), scheduling(7), flower(7), costin(7), yang(6), science(6), problem(6), pollination(6)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 17 - 26
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01003
Web of Science Accession Number: 000459986900003
SCOPUS ID: 85064227660
This paper explores the behavior of the Flower Pollination Algorithm (FPA) and Particle Swarm Optimization (PSO) metaheuristic algorithm in resolving Resource Constrained Project Scheduling Problems (RCPSP) that can model certain practical issues in distributed applications. A RCPSP type problem has at the input a set of activities between which there are precedence relationships and for whose execution it is necessary to allocate resources that are limited. The solution determines the order of execution of the activities with respect to the precedence relations between them and the allocation of the available resources so that the total duration is minimal. The experimental results showed that a near optimal solution can be obtained faster than with other traditional algorithms, mainly for optimization problems in the continuous space. Two versions of FPA and PSO were used, namely combinatorial and priority based optimization. Because during evolution the individuals position changes do not guarantee the precedence order preservation, a new tasks reordering procedure is proposed in this paper.
|References|||||Cited By «-- Click to see who has cited this paper|
| M. Cisse, S. Yalçindag, Y. Kergosien, E. Sahin, C. Lente, A. Matta, "OR problems related to home health care: a review of relevant routing and scheduling problems", Operations Research for Health Care, Vols. 13-14, pp. 1-22, 2017, |
[CrossRef] [SCOPUS Times Cited 155]
 R. M. Chen, C. L. Wub, C. M. Wang, S. T. Lo, "Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB", Expert Systems with Applications, Vol. 37, pp. 1899-1910, 2010,
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 58]
 M. Eddaly, B. Jarboui, P. Siarry, "Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem", Journal of Computational Design and Engineering, Vol. 3, pp. 295-311, 2016,
[CrossRef] [SCOPUS Times Cited 57]
 H. Zhang, H. Li, C.M. Tam, "Particle swarm optimization for resource-constrained project scheduling", International Journal of Project Management, Vol. 24, pp. 83-92, 2006,
[CrossRef] [SCOPUS Times Cited 143]
 K. Bibiks, J. P. Li, F. Hu, "Discrete flower pollination algorithm for resource constrained project scheduling problem", International Journal of Computer Science and Information Security, Vol. 13(7), pp. 8-19, 2015.
 X.-S. Yang, "Flower pollination algorithm for global optimization", in Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, Vol. 7445, pp. 240-249, 2012,
[CrossRef] [SCOPUS Times Cited 1661]
 E. Emary, H. M. Zawbaa, A. E. Hassanien, B. Parv, "Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search", Advances in Data Analysis and Classification, Vol. 11, No. 3, pp. 611-627, 2017,
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 23]
 R. Wang, Y. Zhou, C. Zhao, H. Wu, "A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation", Bio-Medical Materials and Engineering, Vol. 26, pp. 1345-1351, 2015,
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 44]
 S. M. Nigdeli, G. Bekdas, X.-S. Yang, "Application of the flower pollination algorithm in structural engineering", in X.-S. Yang et al. (Eds.), Metaheuristics and Optimization in Civil Engineering, Modeling and Optimization in Science and Technologies, Vol. 7, Springer, pp. 25-42, 2016,
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 50]
 A. Goli, A. Aazami, A. Jabbarzadeh, "Accelerated cuckoo optimization algorithm for capacitated vehicle routing problem in competitive conditions", International Journal of Artificial Intelligence, vol. 16, no. 1, pp. 88-112, Mar. 2018.
 J. Ruiz-Rangel, C. J. Ardila Hernandez, L. M. Gonzalez, D. J. Molinares, "ERNEAD: training of artificial neural networks based on a genetic algorithm and finite automata theory", International Journal of Artificial Intelligence, vol. 16, no. 1, pp. 214-253, Mar. 2018.
 S.-I. Bejinariu, H. Costin, F. Rotaru, R. Luca, C. Nita, C. Lazar, "Parallel processing and bio-inspired computing for biomedical image registration", Computer Science Journal of Moldova, Vol. 22, No. 2(65), pp. 253-277, 2014.
 H. Costin, S.-I. Bejinariu, "Medical image registration by means of a bio-inspired optimization strategy", Computer Science Journal of Moldova, Vol. 20, No. 2(59), pp. 178-202, 2012.
 H. Costin, S.-I. Bejinariu, D. Costin, "Biomedical image registration by means of bacterial foraging paradigm", International Journal of Computers, Communications & Control, Vol. 11, No. 3, pp. 329-345, 2016,
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]
 S.-I. Bejinariu, H. Costin, F. Rotaru, R. Luca, C. Nita, "Image processing by means of some bio-inspired optimization algorithms", Proc. of the IEEE 5th Int. Conference on E-Health and Bioengineering - EHB 2015, Iasi, România, 2015, pp. 1-4,
[CrossRef] [SCOPUS Times Cited 8]
 S.-I. Bejinariu, R. Luca, H. Costin, "Nature-inspired algorithms based multispectral image fusion", Proc. of the 2016 International Conference and Exposition on Electrical and Power Engineering, Iasi, România, pp. 1-5, 2016,
[CrossRef] [SCOPUS Times Cited 23]
 S.-I. Bejinariu, H. Costin, F. Rotaru, R. Luca, C. Nita, "Performance analysis of artificial bee colony optimization algorithm", in Proc. of the 13-th Int. Symposium on Signals, Circuits and Systems, ISSCS 2017, Iasi, România, pp. 1-4, 2017,
[CrossRef] [SCOPUS Times Cited 2]
 X.-S. Yang, Nature-Inspired Optimization Algorithms. Elsevier Inc., pp. 23-173, 2014, ISBN: 0124167438 9780124167438.
 J. Kennedy, R. Eberhart, "Particle swarm optimization", Proc. of the IEEE Int. Conference on Neural Networks, Perth, WA, Australia, Vol. 4, pp. 1942-1948, 1995,
[CrossRef] [Web of Science Times Cited 29791]
 T. Hendtlass, "WoSP: a multi-optima particle swarm algorithm", Proc. of the IEEE Congress on Evolutionary Computation, Edinburgh, Scotland, UK, pp. 727-734, 2005,
 X.-S. Yang, M. Karamanoglu, X.S. He, "Flower pollination algorithm: a novel approach for multiobjective optimization", Engineering Optimization, Vol. 46, No. 9, pp. 1222-1237, 2014,
[CrossRef] [Web of Science Times Cited 376] [SCOPUS Times Cited 471]
 X.-S. Yang, M. Karamanoglu, X.S. He, "Multi-objective flower algorithm for optimization", Procedia Computer Science, Vol. 18, pp. 861-868, 2013,
[CrossRef] [Web of Science Times Cited 207] [SCOPUS Times Cited 296]
 B. Jarboui, M. Cheikh, P. Siarry, A. Rebai, "Combinatorial particle swarm optimization (CPSO) for partitional clustering problem", Applied Mathematics and Computation, Vol. 192, pp. 337-345, 2007,
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 110]
 B. Jarboui, N. Damak, P. Siarry, A. Rebai, "A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems", Applied Mathematics and Computation, Vol. 195, pp. 299-308, 2008,
[CrossRef] [Web of Science Times Cited 218] [SCOPUS Times Cited 280]
 PSPLIB, Project Scheduling Problem Library - PSPLIB, http://www.om-db.wi.tum.de/psplib/main.html (Accessed 4 December 2017).
 R. C. Eberhart. Y. Shi, "Comparing inertia weights and constriction factors in particle swarm optimization", Proc. of the Congress on Evolutionary Computation, La Jolla, CA, USA, Vol. 1, pp. 84-88, 2000,
[CrossRef] [SCOPUS Times Cited 2782]
Web of Science® Citations for all references: 30,811 TCR
SCOPUS® Citations for all references: 6,169 TCR
Web of Science® Average Citations per reference: 1,141 ACR
SCOPUS® Average Citations per reference: 228 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2023-06-06 21:23 in 110 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.