A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
ACO algorithms for the quadratic assignment problem
New ideas in optimization
Introduction to Algorithms
Extensive Testing of a Hybrid Genetic Algorithm for Solving Quadratic Assignment Problems
Computational Optimization and Applications
On Permutation Representations for Scheduling Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Binary particle swarm optimization: a forma analysis approach
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Enhanced forma analysis of permutation problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Geometric particle swarm optimisation
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A design framework for metaheuristics
Artificial Intelligence Review
An algorithmic framework of discrete particle swarm optimization
Applied Soft Computing
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Particle swarm optimisation (PSO) is an innovative and competitive optimisation technique for numerical optimisation with real-parameter representation. In this paper, we examine the working mechanism of PSO in a principled manner with forma analysis and investigate the applicability of PSO on the permutation problem domain. Particularly, our derived PSO schemes are empirically studied based on the quadratic assignment problem (QAP) benchmarks to justify its comparable performance, which in turn implies the benefits of our approach in applying PSO to the discrete problem domain.