Comparing the performance of evolutionary algorithms for permutation constraint satisfaction
Luis de‐Marcos, Antonio García, Eva García‐López, J. Medina, Salvador Otón · 2011
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.