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Comparing the Robustness of Evolutionary Algorithms on the Basis of Benchmark FunctionsDENIZ ULKER, E. , HAYDAR, A.
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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)
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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] [Web of Science Times Cited 3551] [SCOPUS Times Cited 4467]
 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 27967]
 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 14380] [SCOPUS Times Cited 18091]
 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 46] [SCOPUS Times Cited 58]
 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 91] [SCOPUS Times Cited 115]
 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 2000] [SCOPUS Times Cited 2536]
 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 2641]
 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 7]
 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 147] [SCOPUS Times Cited 192]
 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 1278] [SCOPUS Times Cited 1570]
 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 117] [SCOPUS Times Cited 133]
 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 71] [SCOPUS Times Cited 100]
 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 3]
 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 104] [SCOPUS Times Cited 130]
 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 228]
 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 11] [SCOPUS Times Cited 14]
 J. Branke, "Creating Robust Solutions by Means of an Evolutionary Algorithm", Parallel Problem Solving from Nature-PPSN V, 1998, pp. 119-128.
[CrossRef] [SCOPUS Times Cited 147]
 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.
[CrossRef] [Web of Science Times Cited 173]
 Y. Shi, R. Eberhart, "Parameter Selection in Particle Swarm Optimization", Evolutionary Programming VIII. Springer, 1998, pp. 591-600.
[CrossRef] [SCOPUS Times Cited 2634]
 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 1165] [SCOPUS Times Cited 1468]
 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.
[CrossRef] [SCOPUS Times Cited 199]
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