Comparison of Three Evolutionary Algorithms: GA, PSO, and DE
This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief in...
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| Published in | Industrial Engineering & Management Systems, 11(3) Vol. 11; no. 3; pp. 215 - 223 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
대한산업공학회
30.09.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1598-7248 2234-6473 |
| DOI | 10.7232/iems.2012.11.3.215 |
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| Summary: | This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems. KCI Citation Count: 1 |
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| Bibliography: | G704-002162.2012.11.3.003 |
| ISSN: | 1598-7248 2234-6473 |
| DOI: | 10.7232/iems.2012.11.3.215 |