An Empirical Comparison of Particle Swarm and Predator Prey Optimisation
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
Design and Use of the CPAN Branch & Bound for the Solution of the Travelling Salesman Problem (TSP)
CONIELECOMP '05 Proceedings of the 15th International Conference on Electronics, Communications and Computers
Solving Very Large Traveling Salesman Problems by SOM Parallelization on Cluster Architectures
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
An Ant Colony Optimization Algorithm with Evolutionary Operator for Traveling Salesman Problem
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Hardware Implementation of 2-Opt Local Search Algorithm for the Traveling Salesman Problem
RSP '07 Proceedings of the 18th IEEE/IFIP International Workshop on Rapid System Prototyping
Particle swarm optimization-based algorithms for TSP and generalized TSP
Information Processing Letters
A Fast Evolutionary Algorithm for Traveling Salesman Problem
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
An Angle-Based Crossover Tabu Search for the Traveling Salesman Problem
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Mobile robot navigation using particle swarm optimization and adaptive NN
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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The heuristic methods have been widely developed for solution of complicated optimization methods. Recently hybrid methods that are based on combination of different approaches have shown more potential in this regard. This paper also introduces a new method by embedding the idea of particle swarm (PS) intelligence into the well-known method of simulated annealing (SA). This way SA has been capable to search a subspace of the search space by means of an individual particle; therefore the annealing process can start from lower temperatures and use shorter Markov chains for each particle, leading to faster solutions. The results obtained with the proposed method show its potential in achieving both accuracy and speed in small and medium size problems, compared to many advanced methods.