Virus-evolutionary genetic algorithm for a self-organizing manufacturing system
Computers and Industrial Engineering
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
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
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
Collision-Free path planning for mobile robots using chaotic particle swarm optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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This paper presents an improved discrete particle swarm optimization algorithm based on virus theory of evolution. Virus-evolutionary discrete particle swarm optimization algorithm is proposed to simulate co-evolution of a particle swarm of candidate solutions and a virus swarm of substring representing schemata. In the co-evolutionary process, the virus propagates partial genetic information in the particle swarm by virus infection operators which enhances the horizontal search ability of particle swarm optimization algorithm. An example of partner selection in virtual enterprise is used to verify the proposed algorithm. Test results show that this algorithm outperforms the discrete PSO algorithm put forward by Kennedy and Eberhart.