|2/2013 - 10|
Comparing the Robustness of Evolutionary Algorithms on the Basis of Benchmark FunctionsDENIZ ULKER, E. , HAYDAR, A.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (702 KB) | Citation | Downloads: 380 | Views: 1,960|
computational intelligence, evolutionary computation, heuristic algorithms
optimization(14), evolutionary(11), computation(9), algorithm(9), search(7), algorithms(7), harmony(6), applied(6), swarm(5), geem(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2013-05-31
Volume 13, Issue 2, Year 2013, On page(s): 59 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.02010
Web of Science Accession Number: 000322179400010
SCOPUS ID: 84878946831
In real-world optimization problems, even though the solution quality is of great importance, the robustness of the solution is also an important aspect. This paper investigates how the optimization algorithms are sensitive to the variations of control parameters and to the random initialization of the solution set for fixed control parameters. The comparison is performed of three well-known evolutionary algorithms which are Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and the Harmony Search (HS) algorithm. Various benchmark functions with different characteristics are used for the evaluation of these algorithms. The experimental results show that the solution quality of the algorithms is not directly related to their robustness. In particular, the algorithm that is highly robust can have a low solution quality, or the algorithm that has a high quality of solution can be quite sensitive to the parameter variations.
|References|||||Cited By «-- Click to see who has cited this paper|
| Z. W. Geem, J. H. Kim and G. V. Loganathan, "A New Heuristic Optimization Algorithm: Harmony Search", Simulation, Transaction of the Society for Modelling and Simulation International, 2001, pp. 60-68. |
[CrossRef] [SCOPUS Times Cited 2183]
 J. Kennedy, R. Eberhart, "Particle Swarm Optimization", Piscataway: Proceedings of IEEE International Conference on Neural Networks IV, NJ: IEEE Press, 1995, pp.1942-1948.
[CrossRef] [Web of Science Times Cited 17386] [SCOPUS Record]
 R. Storn, K. Price, "Differential Evolution; A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces", Journal of Global Optimization, 1997, vol. 11, pp. 341-359.
[CrossRef] [Web of Science Times Cited 6685]
 J. H. Kim, Z. W. Geem, and E.S. Kim, "Parameter Estimation of the Nonlinear Muskingum model Using Harmony Search", 2001, Journal American Water Resources Assocciation, pp.1131-1138.
 N. T. Melita, S. Holban, "A Genetic Algorithm Approach to DNA Microarrays Analysis of Pancreatic Cancer", 9th. International Conference on Development and Application Systems, 2008, pp. 289-294.
 E. Masehian, D. Sedighizadeh, "Multi-objective PSO and NPSO-based Algorithms for Robot Path Planning", Advances in Electrical and Computer Engineering, 2010, vol.10, no.4, pp.69-76.
[CrossRef] [Full Text] [Web of Science Times Cited 21] [SCOPUS Times Cited 28]
 N. Karaboga, B. Cetinkaya, "Design of Digital FIR Filters Using Differential Evolution Algorithms ", Circuit Systems and Signal Processing, 2006, vol. 25, pp. 649-660.
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 76]
 D. Karaboga, B. Akay, "A Comparative Study of Artificial Bee Colony Algorithm", Applied Mathematics and Computation, 2009, no. 214, pp.108-132.
[CrossRef] [Web of Science Times Cited 915] [SCOPUS Times Cited 1300]
 Y. Shi, R. Eberhart, "Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization", Proceedings of the Congress on Evolutionary Computation, 2000, pp. 84-88.
[CrossRef] [SCOPUS Times Cited 1768]
 A.S.D. Dymond, A.P. Engelbrecht, and P.S. Heyns, "The Sensitivity of Single Objective Optimization Algorithm Control Parameter Values Under Different Computational Constraints", Evolutionary Computation (CEC), IEEE Congress, 2011, pp. 1412-1419.
[CrossRef] [SCOPUS Times Cited 5]
 K. Zaplatilek, M. Talpa, and J. Leuchter, "Optimization Algorithms Testing and Convergence by Using a Stacked Histogram", Advances in Electrical and Computer Engineering, 2011, vol.11, no.1, pp. 11-16.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]
 S. Smit, A. Eiben, "Comparing Parameter Tuning Methods for Evolutionary Algorithms", IEEE Congress on Evolutionary Computation, 2009, pp. 399-406.
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 125]
 M. Mahdavi, M. Fesanghary, and E. Damangir, "An Improved Harmony Search Algorithm for Solving Optimization Problems", Applied Mathematics and Computation, 2007 pp. 1567-1579.
[CrossRef] [Web of Science Times Cited 521] [SCOPUS Times Cited 893]
 A. Ghosh, S. Das, A. Chowdhury, and R. Giri, "An Improved Differential Evolution Algorithm with Fitness Adaptation of the Control Parameters", Information Sciences, Elsevier, 2011, pp. 3749-3765.
[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 69]
 M. M. Ali, P. Kaelo, "Improved Particle Swarm Optimization", Applied Mathematics and Computation, 2008, vol.196, pp. 578-593.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 77]
 A. Lihu, S. Holban, "A Study on the Minimal Number of Particles for a Simplified Particle Swarm Optimization Algorithm", 6th IEEE International Symposium on Applied Computational Intelligence and Informatics, 2011, pp. 299-303.
[CrossRef] [SCOPUS Times Cited 1]
 I. Paenke, J. Branke, "Efficient Search for Robust Solutions by Means of Evolutionary Algorithms and Fitness Approximation", IEEE Transactions on Evolutionary Computation, 2006, vol.10, no.4, pp. 405-420.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 77]
 S. Tsutsui, A. Ghosh, "Genetic Algorithms with a Robust Solution Searching Scheme", IEEE Transactions on Evolutionary Computing, 1997, vol. 1, no. 3, pp. 201-208.
[CrossRef] [SCOPUS Times Cited 193]
 M. R. Saadatmand, M. S. Panahi, and A. A. Atai, "On the Limitations of Classical Benchmark Functions for Evaluating robustness of evolutionary algorithms", Applied Mathematics and Computation, 2010, pp. 3222-3229.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 12]
 J. Branke, "Creating Robust Solutions by Means of an Evolutionary Algorithm", Parallel Problem Solving from Nature-PPSN V, 1998, pp. 119-128.
 R. Storn, "On the Usage of Differential Evolution for Function Optimization", Conference of the North American Fuzzy Information Processing Society (NAFIPS), 1996, pp. 519-523.
 R. Storn, "Differential Evolution Design of an IIR-filter", Evolutionary Computation IEEE, 1996, pp. 268-273.
 Y. Shi, R. Eberhart, "Parameter Selection in Particle Swarm Optimization", Evolutionary Programming VIII. Springer, 1998, pp. 591-600.
 K. S. Lee, Z. W. Geem, "A New Meta-Heuristic Algorithm for Continuous Engineering Optimization: Harmony Search Theory and Practice", Computer Methods in Applied Mechanics and Engineering, 2005, pp. 3902-3933.
[CrossRef] [Web of Science Times Cited 614] [SCOPUS Times Cited 886]
 Z. W. Geem, J.H. Kim, and G.V. Loganathan, "Harmony Search optimization: Application to pipe network design", International Journal of Modelling&Simulation, 2002, vol.22, no.2, pp. 125-133.
 Z. W. Geem, C. Tseng, and Y. Park, "Harmony Search for Generalized Orienteering Problem: Best touring in China", Springer Lecture Notes in Computer Science, 2005, vol.3412, pp.741-750.
Web of Science® Citations for all references: 26,447 TCR
SCOPUS® Citations for all references: 7,695 TCR
Web of Science® Average Citations per reference: 980 ACR
SCOPUS® Average Citations per reference: 285 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 2017-06-26 22:55 in 155 seconds.
Note1: Web of Science® is a registered trademark of Thomson Reuters.
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.