Comparison among five evolutionary-based optimization algorithms
Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques m...
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| Published in | Advanced engineering informatics Vol. 19; no. 1; pp. 43 - 53 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
2005
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1474-0346 1873-5320 |
| DOI | 10.1016/j.aei.2005.01.004 |
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| Abstract | Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping. A brief description of each algorithm is presented along with a pseudocode to facilitate the implementation and use of such algorithms by researchers and practitioners. Benchmark comparisons among the algorithms are presented for both continuous and discrete optimization problems, in terms of processing time, convergence speed, and quality of the results. Based on this comparative analysis, the performance of EAs is discussed along with some guidelines for determining the best operators for each algorithm. The study presents sophisticated ideas in a simplified form that should be beneficial to both practitioners and researchers involved in solving optimization problems. |
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| AbstractList | Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping. A brief description of each algorithm is presented along with a pseudocode to facilitate the implementation and use of such algorithms by researchers and practitioners. Benchmark comparisons among the algorithms are presented for both continuous and discrete optimization problems, in terms of processing time, convergence speed, and quality of the results. Based on this comparative analysis, the performance of EAs is discussed along with some guidelines for determining the best operators for each algorithm. The study presents sophisticated ideas in a simplified form that should be beneficial to both practitioners and researchers involved in solving optimization problems. |
| Author | Elbeltagi, Emad Hegazy, Tarek Grierson, Donald |
| Author_xml | – sequence: 1 givenname: Emad surname: Elbeltagi fullname: Elbeltagi, Emad email: eelbelta@mans.edu.eg organization: Department of Structural Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt – sequence: 2 givenname: Tarek surname: Hegazy fullname: Hegazy, Tarek email: tarek@uwaterloo.ca organization: Department of Civil Engineering, University of Waterloo, Waterloo, Ont., Canada N2L 3G1 – sequence: 3 givenname: Donald surname: Grierson fullname: Grierson, Donald email: grierson@uwaterloo.ca organization: Department of Civil Engineering, University of Waterloo, Waterloo, Ont., Canada N2L 3G1 |
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| Title | Comparison among five evolutionary-based optimization algorithms |
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