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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2019 - 3

 HIGHLY CITED PAPER 

Combinatorial versus Priority Based Optimization in Resource Constrained Project Scheduling Problems by Nature Inspired Metaheuristics

BEJINARIU, S.-I. See more information about BEJINARIU, S.-I. on SCOPUS See more information about BEJINARIU, S.-I. on IEEExplore See more information about BEJINARIU, S.-I. on Web of Science, COSTIN, H. See more information about  COSTIN, H. on SCOPUS See more information about  COSTIN, H. on SCOPUS See more information about COSTIN, H. on Web of Science, COSTIN, D. See more information about COSTIN, D. on SCOPUS See more information about COSTIN, D. on SCOPUS See more information about COSTIN, D. on Web of Science
 
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Download PDF pdficon (1,467 KB) | Citation | Downloads: 999 | Views: 2,401

Author keywords
biological information theory, evolutionary computation, optimization, particle swarm optimization, scheduling algorithms

References keywords
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

Abstract
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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

[1] 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] [Web of Science Times Cited 148] [SCOPUS Times Cited 191]


[2] 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 44] [SCOPUS Times Cited 61]


[3] 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,
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[4] 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 148]


[5] 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.

[6] 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 1919]


[7] 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 26] [SCOPUS Times Cited 28]


[8] 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 40] [SCOPUS Times Cited 49]


[9] 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 36] [SCOPUS Times Cited 57]


[10] 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.

[11] 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.

[12] 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.

[13] 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.

[14] 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]


[15] 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]


[16] 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]


[17] 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 3]


[18] X.-S. Yang, Nature-Inspired Optimization Algorithms. Elsevier Inc., pp. 23-173, 2014, ISBN: 0124167438 9780124167438.

[19] 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 32045]


[20] T. Hendtlass, "WoSP: a multi-optima particle swarm algorithm", Proc. of the IEEE Congress on Evolutionary Computation, Edinburgh, Scotland, UK, pp. 727-734, 2005,
[CrossRef]


[21] 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 414] [SCOPUS Times Cited 528]


[22] 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 223] [SCOPUS Times Cited 320]


[23] 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 83] [SCOPUS Times Cited 110]


[24] 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 222] [SCOPUS Times Cited 286]


[25] PSPLIB, Project Scheduling Problem Library - PSPLIB, http://www.om-db.wi.tum.de/psplib/main.html (Accessed 4 December 2017).

[26] 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 2893]




References Weight

Web of Science® Citations for all references: 33,334 TCR
SCOPUS® Citations for all references: 6,702 TCR

Web of Science® Average Citations per reference: 1,235 ACR
SCOPUS® Average Citations per reference: 248 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 2024-07-17 12:32 in 130 seconds.




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