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AEI
2005

Comparison among five evolutionary-based optimization algorithms

10 years 4 months ago
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 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 f...
Emad Elbeltagi, Tarek Hegazy, Donald E. Grierson
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where AEI
Authors Emad Elbeltagi, Tarek Hegazy, Donald E. Grierson
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