Cited By
View all- Hakli HUguz HOrtacay Z(2022)Comparing the performances of six nature-inspired algorithms on a real-world discrete optimization problemSoft Computing10.1007/s00500-022-07466-1Online publication date: 12-Sep-2022
Comparing the performance of evolutionary algorithms for permutation constraint satisfaction
Authors: 
Luis de-Marcos, 
Antonio García, Eva García, José-Amelio Medina, Salvador OtónAuthors Info & Claims
Pages 57 - 58
Abstract
This paper presents a systematic comparison of canonical versions of two evolutionary algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), for permutation constraint satisfaction (permut-CSP). Permut-CSP is first characterized and a test case is designed. Agents are then presented, tuned and compared. They are also compared with two classic methods (A* and hill climbing). Results show that PSO statistically outperforms all other agents, suggesting that canonical implementations of this technique return the best trade-off between performance and development cost for our test case.
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Index Terms
- Comparing the performance of evolutionary algorithms for permutation constraint satisfaction
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July 2011
1548 pages
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Association for Computing Machinery
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Published: 12 July 2011
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View all- Hakli HUguz HOrtacay Z(2022)Comparing the performances of six nature-inspired algorithms on a real-world discrete optimization problemSoft Computing10.1007/s00500-022-07466-1Online publication date: 12-Sep-2022
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